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Managers' Understanding of Artificial Intelligence in Relation to Marketing Financial Services: Insights from a Cross-Country Study

November 2021
International Journal of Bank Marketing

November 2021

DOI:10.1108/IJBM-09-2021-0440

Authors:

Keele University

Purpose: Given that managers play a crucial role in developing and deploying AI for marketing financial services, this study was aimed at better understanding their awareness regarding AI and the challenges they are facing in providing the attendant technologies, as well as highlighting key stakeholders and their collaborative efforts in providing financial services. Design/methodology/approach: Exploratory, inductive research design. The data was gathered through semi-structured interviews with 47 bank managers in both developed and developing countries, including the United Kingdom, Canada, Nigeria and Vietnam. Findings: Managers are aware of the prospects of AI and are making efforts to address AI as a business need but find that there often exist certain challenges in accelerating AI adoption. The study also presents a conceptual framework of AI in relation to financial service marketing, which captures and highlights the interactions among the customers, banks, and external stakeholders, as well as the regulators. Research limitations/implications: Banks must understand their business objectives, the available resources, and the needs of their customers. Managers should keep the ethical implications of their working relationships in mind when selecting a team or collaborating with partners. In addition, managers should be trained and assisted in comprehending AI in relation to financial services, while the regulators must be involved in the development of AI for financial service marketing. Finally, it is critical to communicate the prospects for AI to consumers. Originality/value: This study provides empirical insight into the opportunities, prospects and challenges pertaining to the use of AI in the area of financial service marketing. It also specifically calls into question certain preconceptions regarding AI and its role in financial services, the chatbots adopted for financial service delivery, and the role of marketing managers in developing AI.

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Managers’ Understanding of Artificial Intelligence

in Relation to Marketing Financial Services:

Insights from a Cross-Country Study

This is the accepted version of the manuscript accepted for publication in the

International Journal of Bank Marketing. Accepted on 28th November, 2021

Emmanuel Mogaji

University of Greenwich, UK

e.o.mogaji@greenwich.ac.uk

Nguyen Phong Nguyen

University of Economics Ho Chi Minh, Vietnam

nguyenphongnguyen@ueh.edu.vn

Cite as:

Mogaji, E. & Nguyen, P. N., (forthcoming) Managers’ Understanding of Artificial Intelligence in

Relation to Marketing Financial Services: Insights from a Cross-Country Study. International

Journal of Bank Marketing.

Managers’ Understanding of Artificial Intelligence in Relation to

Marketing Financial Services: Insights from a Cross-Country Study

Abstract

Purpose – Given that managers play a crucial role in developing and deploying AI

for marketing financial services, this study was aimed at better understanding their

awareness regarding AI and the challenges they are facing in providing the

attendant technologies, as well as highlighting key stakeholders and their

collaborative efforts in providing financial services.

Design/methodology/approach – Exploratory, inductive research design. The

data was gathered through semi-structured interviews with 47 bank managers in

both developed and developing countries, including the United Kingdom, Canada,

Nigeria and Vietnam.

Findings – Managers are aware of the prospects of AI and are making efforts to

address AI as a business need but find that there often exist certain challenges in

accelerating AI adoption. The study also presents a conceptual framework of AI in

relation to financial service marketing, which captures and highlights the

interactions among the customers, banks, and external stakeholders, as well as the

regulators.

Research limitations/implications – Banks must understand their business

objectives, the available resources, and the needs of their customers. Managers

should keep the ethical implications of their working relationships in mind when

selecting a team or collaborating with partners. In addition, managers should be

trained and assisted in comprehending AI in relation to financial services, while

the regulators must be involved in the development of AI for financial service

marketing. Finally, it is critical to communicate the prospects for AI to consumers.

Originality/value – This study provides empirical insight into the opportunities,

prospects and challenges pertaining to the use of AI in the area of financial service

marketing. It also specifically calls into question certain preconceptions regarding

AI and its role in financial services, the chatbots adopted for financial service

delivery, and the role of marketing managers in developing AI.

Keywords: Managers, Artificial Intelligence, Marketing, Qualitative, Financial

Services, Banking

Paper type: Research paper

Page 1 of 49 International Journal of Bank Marketing

Introduction

The introduction of artificial intelligence (AI) in the financial services industry largely

arose from the increasing demand for financial regulation, the need for profitability, and

the competition among firms (Akyüz and Mavnacıoğlu, 2021). Meanwhile, it is also

associated with the advancements in data resources and the associated technologies, as

well as the availability of the necessary financial sector infrastructure. The field of AI

largely involves the adoption of intense computer science applications and is grounded in

the fields of psychology, linguistics, mathematics, and philosophy (Fernandez, 2019). It

has been established as a powerful tool in the provision of financial services, and many

firms are currently utilising various AI-based analytical tools, including machine learning

for data analysis.

Recent research has demonstrated that the number of organisations adopting AI in their

business practises has increased by 270% in the last four years, with nine out of ten

leading businesses making ongoing investments in AI, meaning the global AI market is

expected to reach $267 billion by 2027 (Lin, 2020). According to Insider Intelligence’s

report on the use of AI in the banking industry, most banks (80%) are aware of the

potential benefits presented by AI, and many have implemented AI for risk management

or revenue generation (Digalaki, 2021). However, most companies remain in the learning

phase of adopting AI, continuing to explore how best to integrate various AI systems into

their business operations (Liu, 2020), and this has called for a better theoretical

understanding of the scope of AI and how managers can adopt AI for their business

operations.

While various studies have explored AI in relation to business operations in terms of

marketing and customer engagement (e.g., Davenport et al., 2020; Dwiledei et al., 2020;

Page 2 of 49International Journal of Bank Marketing

Norbert, 2018), there is a growing amount of research on how AI is specifically being

adopted for marketing financial services and how it is enhancing the attendant consumer

experience. For example, Riikkinen et al. (2018) explored the use of chatbots in the

insurance industry, while both Abduladri et al. (2021) and Mogaji et al. (2021) examined

chatbots as a form of digital transformation in the provision of financial services.

Elsewhere, Mogaji et al. (2021) explored the adoption of AI in marketing financial

services in terms of vulnerable customers, while Jang et al. (2021) investigated the

understanding of chatbots among the managers in the Korean financial industry.

Despite this growing body of work, there remain gaps in the knowledge on the use of AI

in the area of financial services marketing. First, the existing studies have predominantly

focused on chatbots as the main feature of AI in this field (e.g., Abduladri et al., 2021;

Mogaji et al., 2021; Riikkinen et al., 2018), often ignoring the other technological

innovations AI has provided, such as credit evaluation tools, credit score evaluation, and

bankruptcy prediction (Donepudi, 2017). Second, the previous studies have often focused

on a specific country without holding a holistic view of the digital transformation ensuing

throughout the industry. For example, Jang et al. (2021) focused on Korean bank

managers, Eren (2021) on customers in Turkey, and Mogaji et al. (2021) on Nigerian

customers. Third, prior research has frequently concentrated on consumers’ adoption and

experiences of using AI systems, while, with the exception of Jang et al. (2021),

researchers have largely ignored the managers’ perspectives on the adoption of AI.

The above present the gaps in knowledge that this study aims to fill through collecting

and analysing data collected from managers operating in various different countries –

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from both the developed and the developing worlds – and across different sectors within

the financial services industry.

Given that the managers play a crucial role in developing and deploying the AI systems

and ensuring that the customers adopt these technologies (Jang et al., 2021), it is essential

to understand the perspective and challenges they are facing in providing these

technologies. Therefore, this study makes a significant theoretical contribution to our

understanding of AI in relation to financial studies, extending the previous studies, such

as those from Mogaji et al. (2020), Riikkinen et al. (2018), and Jang et al. (2021), by

exploring AI from a different perspective. Specifically, the study presents a conceptual

framework for AI in relation to financial service marketing research, highlighting key

stakeholders and their working relationships in providing customers with enhanced

financial services (see Table 1 for summary of key studies and contribution of our study).

Furthermore, the study provides various managerial implications for financial service

providers and Fintech developers in terms of the prospects, opportunities, and challenges

pertaining to the adoption of AI for financial service marketing.

Literature review

The application of artificial intelligence for marketing

While it is widely recognised that AI is applicable and being adopted in terms of

numerous facets of human endeavour, this paper focuses on adopting AI for marketing

and consumer engagement within the financial services industry. To date, AI has

dominated a number of technology-centred segments, and its influence will soon manifest

in of marketing. While Norbert (2018) chose to question the feasibility of developing AI-

based marketing insights, a number of recent studies have highlighted the enormous

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prospects of AI in terms of improving marketing strategies. For example, Dwiledei et al.

(2020) presented a seminal work exploring the significant opportunities and prospects of

AI in marketing and other business practices, with the authors acknowledging that AI will

shape the future of the industry and society at large. This notion was further corroborated

by Davenport et al. (2020), who postulated how AI will change the future of marketing,

citing the algorithms’ capacity to automate business processes, analyse Big Data, and gain

insight from both customer and transaction data.

The integration of AI in the area of digital marketing was largely influenced by the

advances in computing power and the emergence of Big Data (Haenlein and Kaplan,

2019). With various AI tools, organisations can better understand the situation and

subsequently implement effective target marketing strategies (Mogaji et al., 2020).

Meanwhile, the onset of digital technologies has compelled marketers to reconsider the

traditional marketing techniques, with digital marketing regarded as being a more viable

alternative (Saura et al., 2019). According to Mogaji et al. (2020), AI provides viable

tools for analysing the precarious conditions of vulnerable customers and the suitable

contact methods for implementing digital marketing for this segment.

The application of AI and machine learning in marketing allows for capitalising on the

capacity for establishing a connection between computing power and the vast human

insights. According to Ma and Sun (2020), AI agents powered by machine learning are

continuously transforming the business world. This transformation largely relies on the

combined capacities of AI and machine learning, which are used in the areas of marketing

research, data analysis, tracking, and networking, to establish consumer purchase patterns

and connect them to specific marketing theories. For their part, Shahid and Li (2019)

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established that the application of AI in marketing helps to improve business performance

through its deployment in terms of various marketing functions. In fact, AI is being used

to develop numerous marketing strategies and other marketing-related activities, such as

product promotion, pricing, planning, development, and distribution (Mogaji et al., 2020).

The application of artificial intelligence in financial services

The major application of AI has been the provision of various financial services, with its

deployment in this field allows for the capitalisation on increasing possibilities of AI that

have developed over recent years (Fernandez, 2019). In fact, the attendant advancements

have made it possible to develop numerous practical applications that serve various needs

in the financial services industry, including the automation of various tasks and the

expansion of the analytical capacity. Here, the efficiency is increased, the quality is

enhanced, customer satisfaction is improved, and the operation costs are reduced

(Grigoroudis et al., 2012). Meanwhile, repetitive tasks can be easily automated using AI,

with such an intervention minimising human error while boosting productivity. The

technology is also deployed in the financial services industry to enhance analytical

capacities since it enables financial service providers to analyse larger volumes of both

structured and unstructured data (Kruse et al., 2019), allowing for the analysis of a larger

number of variables, thus enhancing the overall quality of the analysis. In short, the

capabilities of AI allow financial institutions to provide better services and increase the

financial inclusion for more customers, while it can also lead to cost reductions in various

areas, such as regulatory compliance.

Meanwhile, AI is deployed in terms of various applications in the banking sector,

including smart wallets, voice-assisted banking, banking-process digitisation, and

blockchain hosted payments (Donepudi, 2017). Here, smart wallets represent the

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advancements in banking services that address the need for customer satisfaction and

increased accessibility. Having been widely accepted by various service providers and

vendors, smart wallets reduce the dependency on cash payments and decrease the levels

of monetary use. Meanwhile, voice-assisted banking largely involves the use of voice

commands and touch screens to access banking services (Weill and Woerner, 2015) and

accommodates the increasing need for flexibility, especially in choosing suitable regional

languages, accessing vital information, and connecting to different financial services.

This form of banking is also advantageous since it reduces the possibility of human error

while enhancing systemic efficiency. According to Donepudi (2017), digitising the

banking processes using AI is also advantageous since it reduces the lines in banking

facilities and increases overall productivity. Finally, blockchain hosted payments largely

involve capitalising on the capabilities of AI to implement real-time payment processes

and speed up the payment procedures, significantly enhancing both customer support and

customer satisfaction.

The deployment of AI in the banking sector has largely capitalised on the fact that banks

have always been early adopters of technology, with modern technologies having been

deployed in the banks for both front-end and back-end applications (Smith and Nobanee,

2020). Data is a crucial component in all the business factions of the banks, and the huge

data management requirement has accelerated the adoption of AI in banking services.

Here, the technology is deployed for autonomous data management services, eliminating

the need for human involvement to maximise the speed, accuracy, and efficiency, while

it is also deployed for the real-time identification of customers and the prevention of fraud

in the realm of online banking (Wei et al., 2012). Indeed, it has presented a crucial

initiative aimed at addressing the escalating challenge of credit card fraud, which has

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largely negated the gains made through the advancement of online payments. In terms of

being used to verify the identity of clients, the algorithms scan the relevant documents to

assess the reliability of the information provided (Smith and Nobanee, 2020). Analysing

legal documents and extracting important clauses have also been facilitated via the use of

AI technology. Overall, the technology presents a continually advancing system whereby

the AI algorithms autonomously analyse data to enhance accuracy.

The introduction of AI in the banking sector has also helped address the systemic

challenges associated with the quality of services, especially where making accurate

decisions is essential. Here, AI is efficient since it eliminates any possibility of human

error (Donepudi, 2017). In addition, the quality of service is mainly assessed in terms of

service times, and the use of AI has enabled banks to shorten their service times by

overcoming the traditional limitations of the statistic models (Fernandez, 2019). The use

of deep learning is another viable intervention that will undoubtedly transform customer

services, essentially using specific patterns to predict future activity, predictions that are

highly valuable to stockbrokers, asset managers, and investment bankers alike.

The application of artificial intelligence for customer engagement

Chatbots constitute one of the major applications of AI for customer services in the

financial services industry, generally being deployed for customer service management

and the provision of advisory services (Ba et al., 2010). The use of chatbots in financial

services constitutes the aspect of self-service technology adoption, which aims to increase

the interaction between the consumer and the technology without necessarily involving

the service providers (Jang et al., 2021). Such an intervention is viable in providing

customer services for various sectors, including insurance and banking. Chatbots are

essentially text-format applications, the popularity of which has been increasing

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following the increased use of messaging apps, which have been growing due to the

technological advances and the emergence of the millennial generation. In short, the

millennials are more attracted to the use of chatbots for customer services since they

prefer to chat, which reduces the need for direct conversations (Quah and Chua, 2019).

In fact, this preference for chatting makes it easier to address any customer service queries

and other inquiries using AI chatbots (Abdulahi et al., 2021). The chatbots are also

attractive due to the indirect nature of the communication alongside other advantages,

such as the lack of language barriers, lower operating costs, and 24-hour operations

(Klaus and Zaichkowsky, 2020). Indeed, many firms have identified the potential of

chatbots in the area of customer services, despite the fact that huge investments are

required to develop viable solutions for integration in the legacy system. Financial

institutions are the most active in adopting chatbots since they are regarded as a viable

tool for facilitating digital transformation. Banks have also introduced chatbots that offer

various services to the customers, including checking their account balance information,

assisting with their transactions and bill payments, and receiving important

communications. Chatbots can also provide vital information to the customers, such as

real-time sales notifications, informing users about specific services offered, and making

suggestions based on specific contextual information (Belanche et al., 2019). Meanwhile,

in the Korean financial industry, chatbots have been deployed for product inquiries,

funding purchases and repurchases, making consultations, and conducting overseas

money transfers (Jang et al., 2021). In short, the management teams of financial service

providers expect that the deployment of chatbots in customer services will improve the

overall customer experience.

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Virtual assistants can help resolve the customer engagement challenge through

conversing with the customers through messaging platforms and mobile applications

(Brill et al., 2019), answering customer queries, offering fundamental advice, and

addressing common transactions and inquiries (Killa, 2014). Specific AI technology is

also deployed in the area of claims management to reduce the overall time taken to

process the claims as well as the handling costs while enhancing customer experience.

Indeed, various AI-powered claims assistants have been utilised to speed up the process

of verifying and processing insurance claims, with many predicting that this technology

will enable instant payments for claims, especially in terms of motor vehicle insurance

claims. Certain AI tools are also valuable for risk assessment tasks, often proving vital

for speeding up the risk underwriting process (Hall, 2017) through assisting in the rapid

analysis of the various demographic components for underwriting, including age, health

history, gender, and lifestyle factors.

Meanwhile, AI-based detection systems are suitable for credit card detection systems due

to their sophisticated modes of operation and real-time deployment. Automatic credit card

fraud detection systems use Bayesian and neural networks grounded in machine learning

(Raj and Portia, 2011). Here, artificial neural networks (ANN) are initially trained using

the normal behaviour of ordinary cardholders, which allows for the subsequent detection

of suspicious activity and the deployment of suitable prevention strategies. Bayesian

networks are essentially belief networks consisting of AI programming that combines

data mining and machine learning algorithms. Such neural networks are highly

advantageous due to their learning capabilities, which eliminates the need for

reprogramming. The use of AI in credit card fraud detection is largely based on the high

frequency of credit card transactions, which provides the large amounts of data required

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for the training, back-testing, and validation of the algorithms (Bao et al., 2020). The

technology also presents a rapid means to combat fraud, avoiding the limitations

associated with more prolonged human response procedures. In short, AI is adopted in

the field of credit card fraud prevention since it is less costly.

Theoretical Underpinning

The theoretical background of this study is grounded in three fundamental studies. The

first was conducted by Mogaji et al. (2021), who devised a conceptual framework

pertaining to marketing financial services in relation to vulnerable customers. Overall,

the study highlights the ethical implications of data collection and processing, placing

huge responsibilities on managers in terms of understanding how their data are being

collected and how consumers are targeted using AI and programmatic. The second study

was conducted by Ruyter et al. (2018) and revolved around the conceptual exploration of

the digital surrealistic approach for deploying AI-based analytics and machine learning

to explore customer data and support decision-making. Here, the authors recognised the

need to understand the enormous benefits of AI for business operations and how this can

translate into actual value for the organisation, which entails the need for managers to

understand what AI means and offers as well as how best to use it. The third study

involves the development of a conceptual framework by Riikkinen et al. (2018), which

presents the prospects of AI in creating value in the field of insurance. Here, the focus

was on chatbots and how customer data can be extracted for a value creation that benefits

the consumers in terms of product offering and service quality.

Within the context of the present study, the aforementioned theoretical developments are

adapted to explore the managers’ awareness of AI and how the technology can be used

in their business operations, with a specific focus on exploring the process and any

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inherent challenges the managers are facing. Despite the increasing adoption in customer

service management, the success of AI in the provision of financial advisory services

remains open to discussion (Zhang et al., 2021). The major factors contributing to the

lack of adoption relate to the level of expertise, trust, intention of use, and performance

expectations (Abdulahi et al., 2021, Soetan et al., 2021). According to Longoni et al.

(2019), many consumers hesitate to use AI-based technologies for important and high-

risk domains when high-level human expertise is available. This further highlights the

need for managers to understand the context of AI, the attendant challenges, and its

acceptance by consumers.

The financial service providers have access to the Big Data related to the customers

(Dwiledi et al, 2021). There is a growing acceptance among the customers to freely

provide their data (Mogaji et al., 2020). However, it is important to recognise some of the

major challenges associated with the use of AI, which include consumer privacy issues

and technological complexity. Consumer privacy is a major concern due to various

legislations pertaining to the security of customer data (Kruse et al., 2019), while the AI

applications in the financial services sector entail the major challenge of governance

process design (Gillath et al., 2021) and regulatory requirements (Wall, 2018). This

notwithstanding, there are huge prospects for AI, and managers must be able to find their

way around these challenges. Therefore, the present study was aimed at exploring the

lived experiences of managers as they aspire to effectively adopt AI in view of marketing

their financial services (see Table 1 for a summary of key studies and contribution of our

study).

____Table 1_____

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Page 13 of 49 International Journal of Bank Marketing

Methodology

Explorative and inductive approach

An exploratory, inductive research approach was adopted for this study to provide insight

into managers’ understanding of the use of AI in marketing financial services.

This approach was adopted to allow deeper insight into the managers’ work

experiences, decision-making processes, and interactions with other stakeholders

involved in developing AI systems for their business operations. Specifically,

this qualitative approach is relevant for this study since it was aimed at, as Johnson et

al. (2006) put it, ‘capturing the actual meanings and interpretations that participants

subjectively ascribe to phenomena in order to describe and explain their

behaviours’ (p. 132). In fact, this exploratory research approach has been

previously adopted in understanding bank managers’ involvement in various

business operations. Here, Mogaji et al. (2021) interviewed a number of bank

managers in Nigeria to better comprehend their financial inclusion strategies, while

Deigh and Farquhar (2021) interviewed various managers to ascertain the impact of

their corporate social responsibility initiatives. Larsson and Viitaoja (2017)

interviewed Swedish banks managers to explore their banks’ digitalisation

processes. According to Bamforth et al. (2018), the qualitative method is suitable for

our context as we are in search of an in-depth answer to a specific question, seeking to

‘understand phenomena through accessing the meanings that participants assign to

them’ (Orlikowski and Baroudi, 1991, p. 5).

The sample

The target participants are individuals working within the financial services industry in

managerial roles, being responsible for sourcing, developing, implementing,

and deploying marketing and digital transformation strategies involving AI. The

participants were sought across different countries and various financial service

providers in terms of

various levels of experience to ensure a diversity of views and experiences to enrich the

data and subsequent findings. The managers included not only individuals working in

banks but also individuals working in Fintech and payment services companies. Great

efforts were made to reach out to different managers across different countries to ensure

a multi-country approach was applied in the study. Here, we targeted participants from

the UK (Europe) and Canada (North America) to represent developed countries and from

Nigeria (Africa) and Vietnam (Asia) to represent developing countries and emerging

markets. The participants were initially contacted through the personal networks of the

research team with the use of snowballing sampling. In addition, potential participants

were targeted through LinkedIn, specifically those with managerial roles in finance,

marketing, and information technology across the four different countries. One hundred

twenty-seven prospective participants were contacted through the inMail function on

LinkedIn. They were then provided with details regarding the research context and the

background of the study before their participation was requested for interviews that would

be carried out online. We recognise the possibilities of self-selection bias in our sample

and made an effort to address this by seeking a defined target population and a sampling

frame, contacting different managers from different countries, and following up on non-

responders, making the study representative by including as many people as possible

(Young et al., 2020, Czarnecka & Danbury, 2018). In total, 31 participants responded and

confirmed their willingness to participate in our research project. An additional 16

participants were recruited through personal contact, making the total number of

participants 47, all of whom were managers working in the four countries. The

participants’ demographic information is presented in Table 2.

_____Table 2_____

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Data collection

Qualitative data were collected through semi-structured interviews conducted with the

participants. The interview questions were developed based on the existing literature,

with a specific focus on the conceptual frameworks developed by Mogaji et al. (2020),

Riikkinen et al. (2018), and Jang et al. (2021). The questions were presented in an open-

ended form to allow the participants to express themselves and increase the possibilities

of capturing more emerging ideas (Bamforth et al., 2018). The questions were tested using

a pilot study with six managers who were not part of the final sample. The pilot study

allowed us to refine the scope of the questions, improve the manner in which the questions

were asked, achieve clarity and monitor the timing, duration, and flow of the interviews.

The pilot interview participants did not identify any serious flaws or any sensitive issues

with the interview protocol but did provide some feedback on the type of questions and

the flow of the interview. In response to their feedback, we made a few changes to the

wording, format, and arrangement of the questions.

Given that the participants were in different countries and time zones, the interviews were

conducted virtually using Zoom. This part of the work was completed by the first author.

The interviews began with a brief explanation of the objective of the study and the ethical

considerations. Even though the participants had signed a consent form via email, verbal

consent for their participation and for recording the interview were sought prior to the

interview on Zoom. The participants were informed that they could leave the interview

at any point without giving any reasons, and they all agreed that the interviews could be

recorded to aid the transcription and allow us to better understand their comments.

The participants were asked the same broad and open-ended questions about their

understanding of the use of AI in financial services, their roles in developing the AI

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systems in their organisations, their efforts in deploying AI for marketing financial

services, and the challenges they have faced in integrating AI in these services. The

interview session also provided the managers with an opportunity to ask probing,

clarifying questions to expand on any emerging themes, while they were also allowed to

ask questions at the end to allow for a more comprehensive discussion (Bamforth et al.,

2018; Farinloye et al., 2019). The interviews lasted between 58 and 83 min (median 67

min), with the recorded interviews transcribed by a professional and saved in PDF form

for further analysis. Additional notes were taken during the interview and were included

as part of the dataset for further analysis.

Data analysis

The transcribed data were analysed following the thematic analysis guidelines suggested

by Braun and Clarke (2006). This began by initially reading the transcribed interviews

intensively and repeatedly to gain a better understanding of the participants’ responses

(Farinloye et al., 2019). This was followed by uploading the interview transcripts into

NVivo, a qualitative data analysis (QDA) computer software package. The transcripts

were reread using the software, and the data were carefully examined to identify any

related concepts and categories (Deigh and Farquhar, 2021). Meanwhile, Auto-transcribe,

an automated transcription technology function used to assist in the analysis of extensive

datasets, was also run on our dataset. Following the auto-transcription, the transcripts

were subsequently manually annotated using the software. The editor function was used

to review and change the transcripts, highlight relevant codes and create specific nodes

as they emerged from the dataset. These codes were subsequently grouped into relevant

themes that pertained to the objectives of the study. The frequency of the themes across

all the transcripts indicated how important they were. A number of the themes were also

expanded to reflect new emerging themes, while others were removed or merged since

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they did not have enough content and quotes to stand alone as core themes. At this stage,

any hierarchical and associative relationships among the codes and themes were also

established (Soetan et al., 2021). The final themes and sub-themes were identified and

shared with other research teams and professional colleagues to provide a quality check

and additional insights. No further changes were made at this stage, and the 23 sub-themes

were merged into five key themes pertaining to the three main objectives of the study.

Data credibility

Considerable efforts were taken to ensure the credibility of the data. First, the ethical

guidelines of the authors’ affiliated institution were followed, including in terms of

consent, data security, and confidentiality (Deigh and Farquhar, 2021). Second, we

verified the role of the participants to ensure that they were giving us the right information

based on their roles. This information was verified using LinkedIn, any news coverage

announcing their appointment or media coverage of their business operations, as well as

the participants’ profiles on their company websites. Third, the participants were allowed

to control their involvement in the projects, which ensured that they were willing to give

information that they were comfortable with and not forced or put under undue pressure

to disclose any information. Fourth, we shared the transcript interviews with the

participants to ensure that we had correctly covered and transcribed the information they

had provided (Czarnecka & Mogaji, 2020). This is described as a ‘member check’ and is

regarded as one of the main ways to ensure the credibility of a qualitative study (Merriam

and Tisdell, 2015). Fifth, the different stages of the data analysis were presented through

an audit trail and a rigorous peer debriefing process through managing the process with

the team and receiving quality assurance of the data from other colleagues (Miles et al.,

2013). Sixth, based on the in-depth description recommended by Lincoln and Guba

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(1985), we provided a detailed description of the quotes from the interviews to reinforce

each point.

Findings

The qualitative data analysis revealed three key themes surrounding the managers’

understanding of the use of AI in marketing financial services. While there were

variations in terms of the experience based on the type of financial services, the years of

experience, and the participants’ country, the main themes were as follows: (1) awareness

of the need for AI, (2) addressing the growing need for AI in their business operations,

and (3) accelerating the adoption for enhancing business operations. These themes are

discussed below and supported with quotes from the managers.

Awareness regarding artificial intelligence

The managers recognised the importance of AI as a tool for business transformation and

how it would change their business operations, highlighting that AI is becoming an

integral part of the conversation surrounding digital transformation and consumer

engagement within the financial services industry. The participants also noted that AI is

not only about marketing but also about developing financial products and services, such

as cryptocurrency, blockchain, and financial engineering. For example, one of the

managers working with a commercial bank in Canada alluded to the idea that ‘AI is now

vast in financial services, involving the design and marketing of financial services’.

However, the managers also raised a concern regarding the ongoing misconceptions about

the prospects and possibilities of AI, with many people remaining unsure about AI and

continuing to confuse the basic technology used in banking with AI, noting that

everything could not just be AI. One manager described it as follows:

Page 18 of 49International Journal of Bank Marketing

[AI is] a buzzword that everyone is using and talking about, but few people know

what it is and how it actually works. Everyone has a different understanding of

AI, depending on their level of experience and exposure.

These misconceptions were also highlighted among the managers from the developing

countries, who noted that not many people are aware and involved with AI and can often

simply claim they know how it works. Some of the managers even noted that not everyone

who works within financial services knows about AI and how it truly works.

Chatbots also emerged as one of the most common examples of AI used in financial

services. Here, the managers recognised that having a chatbot on WhatsApp or Messenger

to engage with the customers is like an entry-level form of AI in financial services, while

they did acknowledge that this type of application had transformed financial service

provisions. However, they noted that there is more to AI than merely chatbots, noting that

a chatbot is just one of the many possibilities of AI. One participant from the UK stated

the following:

[h]aving a chatbot on your website is now very easy and it is becoming a

standard; but you need to know that chatbot is not all, that’s just about

communicating; ultimately, you need to be able to collect data from the chatbot

and use it to inform your business strategy.

This was further corroborated by another participant from Nigeria:

…chatbot is a good way to start but we know that is just the entry-level. We are

still doing more with AI in the banking sector.

Overall, the managers acknowledged that chatbots appear to be the only physical aspect

that the customers are aware of when it comes to the AI used in financial services, and

Page 19 of 49 International Journal of Bank Marketing

there is little awareness on the part of the customers of the various other algorithms

working behind the scenes and influencing the decisions for the customers.

The awareness that there are many more possibilities for the use of AI in financial services

ensured that the managers recognised the importance of exploring other options to meet

customer needs and improve their knowledge of AI. In short, the managers realised that

they need to increase their awareness about AI, what it offers, and how to adopt it for

their business:

[w]ith the many misconceptions about AI and the growing drive for digital

transformation in this sector, it is important for staff to be very conversant with

this technology, or at least to know the theory and how it really works.

Addressing artificial intelligence as a business need

Following on from the awareness of the prospects of AI, the managers shared their

thoughts regarding the measures being put in place to integrate AI in view of addressing

their business needs. Here, one of the managers noted the following:

…it’s not just about recognising how it works but, importantly, seeing how it

works in your company. You want to try things out and improve the customers’

experience.

While many managers agreed that the need for AI in their operations appears to evolve

automatically and naturally as the need arises, some could not identify the exact time they

decided to use AI. However, the integration of AI in business practices evolved as part of

their organisations’ transformation strategies. Three managers working in Nigeria noted

that they had observed the growing trends, had shared them with their managers and had

provided some direction on how to go about addressing them, while seven of the

participants noted that the idea for AI was pitched to them by various Fintech companies.

Page 20 of 49International Journal of Bank Marketing

Ultimately, the managers identified three main challenges in addressing the business

needs for AI: convincing the management team that it is needed, assembling the right

team to do it, and deploying the system and making sure it works within the existing

infrastructure. The managers also noted a need to justify that the AI system is necessary

for effective marketing and delivering financial services, which often includes sharing

examples of other banks – both within the country and outside it – that have adopted AI.

The managers further reiterated that the lack of awareness about AI at the management

level also presents a challenge for the quick adoption and deployment of AI applications,

with one manager from Nigeria stating the following:

…when your boss does not know much about these things, it becomes harder to

convince them that it will work; sometimes, they think it’s an idea of the

millennials and therefore consider it to be expensive and extravagant.

Even when they have received authorisation from the banks’ management team to

continue developing the AI system, the managers felt that assembling the team to work

on the project could be challenging, especially for the marketing managers who often feel

that they lack the technical abilities to get involved in the project or even lead the team.

Here, the managers noted that they often invite the Fintech companies that originally

pitched the idea to collaborate with their in-house team at the bank, while, in other cases,

they sought out another team that could do the job better and possibly is more cost-

effective and scalable, or hired additional staff members to aid the in-house team in

completing tasks.

Page 21 of 49 International Journal of Bank Marketing

Irrespective of the approach taken to assemble the team, the managers also noted that

managing a team comprised of individuals with varying technical capabilities could pose

a challenge, especially when the marketing team shares their expectations and the

developers state what is realistically possible. The managers shared various instances of

when collaboration between the teams became difficult and how they brought in a

consultant to help resolve the issue. The managers further reiterated the conflict between

their marketing expertise and collaborating with tech developers, albeit that, ultimately,

they all want the same thing.

Accelerating the adoption of artificial intelligence

The managers acknowledged that AI in marketing financial services is here to stay and

that it is, therefore, vital for them to accelerate its adoption to enhance their business

operations and maximise profits through enhanced services. They did, however,

recognise the role of regulators in achieving this, emphasising the importance of the

regulators in helping to ensure that all stakeholders are kept informed. Here, the

participants in the UK acknowledged the good works of the Financial Conduct Authority

(FCA), the regulatory body of UK banks, and what this conduct regulator was doing to

raise awareness about the implications of AI in financial services. One participant noted

the following:

[t]he FCA is not just interested in the marketing but the overall impact of AI in

financial services, including in terms of data quality and ethics.

However, this is not the case for many of the managers from the developing countries,

who had varying perspectives and expectations of their regulators, with one participant

from Nigeria stating the following:

Page 22 of 49International Journal of Bank Marketing

I don’t think the CBN [Central Bank of Nigeria] is aware of AI financial services

as I have not seen any circular on information from them; the last thing I heard

was about the cryptocurrency ban, but we know that’s not really about AI.

The managers also highlighted the need to ensure the customers ‘buy-in’ to accelerate the

use of AI, recognising the value of data and the importance of encouraging consumers to

provide additional data and, importantly, to adopt the technological innovations that are

being offered. The managers observed an increasing level of advertisement and marketing

communications aimed at informing people about chatbots and other forms of digital

transformation. In addition, the managers believed that the customers should have

confidence that banks are looking out for the customers’ best interests.

The bank managers themselves also stated that they need to acquire more knowledge and

understanding of AI and how it affects their e-business and operations as it becomes

increasingly necessary for them to engage in conversation. The managers also suggested

that this knowledge acquisition regarding AI should also extend to the top management

level as this could assist in driving down costs and cascading the digital transformation

strategies.

The human resource aspect is also instrumental in accelerating the adoption of AI in terms

of bringing onboard individuals who understand what is appropriate, working with the

right technical partners, and ensuring that matters are conducted ethically. One participant

from Nigeria noted that,

…the last project we did had to be outsourced to some guys in India; we had little

control over the design process; we simply trusted that they did a good job.

Page 23 of 49 International Journal of Bank Marketing

These participants acknowledged that having individuals with the right skill sets to do

this job is important. Here, another participant noted the following:

[w]e must acknowledge that we are marketers, we are not developers. We always

need to have the right team to get things done.

This recognition aligns with earlier comments related to improving the team, which

includes outsourcing the project to a competent team or recruiting more individuals to

bolster the capabilities of the in-house team.

The country in which the organisation operates also influences the extent to which AI

adoption is accelerated, with the data analysis revealing a significant difference in

experience between the participants from developed countries and those from developing

countries. Specifically, the participants from the developed countries were more

confident about the trajectory of AI, with one participant from the UK stating the

following:

[v]ery soon, we won’t need high street banks; all banking operations will be

carried out on mobile phones.

In contrast, the participants from the developing countries were more pessimistic, being

largely concerned about the regulatory framework, the level of technological

development in their country, the consumers’ attitude towards technology, the data and

infrastructure required to support AI algorithms, the size of their financial services

industry, and the manpower required to champion the innovative ideas. One participant

from Nigeria concluded with the following:

[w]e know the world is moving fast with AI, but we will catch up at our own pace.

Page 24 of 49International Journal of Bank Marketing

Discussion

According to our qualitative study, the bank managers – both from the developing and

the developed countries – were aware of the prospects of AI in the area of marketing

financial services. They all recognised that AI had brought a much-needed digital

transformation into the provision of financial services. While the role of chatbots is well

recognised in this provision, the managers recognised that they are not the only type of

AI system needed and highlighted a variety of other ways to integrate AI into financial

services marketing. There remain, however, various concerns related to managers'

expectations regarding the development of AI algorithms, the role of the regulators, and

the inherent challenges pertaining to the consumers’ adoption of AI systems.

Theoretical contributions

While we recognise the variety of work on how AI is shaping marketing practices (e.g.,

van Esch and Stewart, 2021; Davenport et al., 2020; Dwiledi et al., 2020, Wayne et al,

2020), this study specifically contributes to the areas of marketing that are focused on AI

and marketing financial services. Specifically, we provide insight that extends the work

of Riikkinen et al. (2018) on using AI in creating value in insurance service provision as

well as that of Mogaji et al. (2021) on theoretically positioning the implications of AI in

terms of digital marketing of financial services to vulnerable customers. Beyond the

conceptualisation and synthesis of the existing academic literature, this study provides

empirical insight into the opportunities, prospects, and challenges pertaining to the

adoption of AI in the field of financial services marketing. (see Table 1 for a summary of

key studies and contribution of our study).

Our study further presents a conceptual framework of AI in relation to financial services

marketing. The framework presented in Figure 1 identifies various key constructs and

Page 25 of 49 International Journal of Bank Marketing

stakeholders related to adopting AI for financial services marketing. This framework and

its implications build on the analysis of the qualitative data collected during the interviews

with managers as well as the existing theoretical studies, such as those from Mogaji et al.

(2021), Riikkinen et al. (2018), Dwivedi et al. (2021) and Ruyter et al. (2018), as

identified in the theoretical underpinning section of this study.

Figure 1. Conceptual framework of the use of AI in financial services marketing

The main constructs here are data collection, aggregation, and processing. Our framework

demonstrates that data are being collected from the customers to develop AI algorithms

for banks and other financial services providers. The data are either willingly provided

by the consumers when opening a bank account or searching for products on comparison

websites or through leaving a digital footprint via credit agencies and government

organisations (Mogaji et al., 2020; Riikkinen et al., 2018). Key external stakeholders are

presented here as being responsible for aggregating the collected data, which may not

Page 26 of 49International Journal of Bank Marketing

necessarily be the bank managers. For example, marketing technology (MarTech)

companies may integrate certain elements of advertising agencies and engage with digital

marketing and programmatics to deliver advertisements to individuals based on their

browsing history or existing information (Mogaji et al., 2019). In addition, there exist

various partners responsible for aggregating this information and making it useful for the

banks and other financial institutions. For example, credit agencies such as Experian

collect and aggregate information from millions of people in the USA, the UK, Brazil,

Colombia, and India, and assist organisations worldwide in lending in a more responsible,

fairer, and rapid manner to both individuals and businesses (Experian, 2021). Moreover,

web analytics companies such as Google (Analytics), Facebook (Insights), and Crazy

Egg, as well as the various comparison websites, collect data from customers that are

subsequently fed into the data aggregation process. These aggregate data are made

available for developers – including data scientists – to develop algorithms that can shape

the marketing and availability of a given financial product. In addition, it is crucial to

recognise the need for data processing to ensure that the data collected by the banks and

external stakeholders are presented in a format that can be used to make business

decisions.

The information owned by banks (voluntarily provided by customers) and the aggregated

data collected from external stakeholders are being used for service delivery purposes,

which includes introducing customers to a different account, detecting fraud when a new

account is opened, and providing relevant answers to customers’ queries via chatbots.

Here, we observed that bank managers may lack control due to the inherent design and

development of the algorithms, which are frequently performed by the developers and

partners. Accordingly, there are certain prospects for marketing financial service

Page 27 of 49 International Journal of Bank Marketing

products, especially in terms of personalised banking, recommendations, offers, and

promotions. These are often presented automatically as a result of machine learning and

AI’s understanding of customers’ past financial transactions and their current financial

state. Lastly, as part of the financial services marketing opportunities, this information is

being used product development and ensuring the right customers are introduced to the

right product. This enables banks to have an overview of their business operations and to

identify how they can best serve their customers.

As reported by our participating bank managers, the role of the regulators cannot be

overlooked, especially as they serve as control measures between the engagement of the

consumers and the banks. The managers acknowledged that the regulators are in place to

provide overarching support and oversee the marketing process, as well as to ensure that

the industry benefits from the innovation brought about by AI. These responsibilities of

the regulators are important for two reasons. First, as always having been part of a

regulated industry, even prior to the advent of AI and the digital transformation, financial

services providers are expected to regulate their marketing strategies (Mogaji, 2018) to

ensure that all the adverts are ‘clear, fair and not misleading, regardless of the media type’

(FCA, 2021). Second, AI has brought additional concerns regarding the monitoring of the

practices of financial services providers. Here, the FCA acknowledges that the use of data

analytics and AI is growing and must be monitored to protect the customers’ interests

(FCA, 2021b). Meanwhile, Canada’s Office of the Superintendent of Financial

Institutions (OSFI) also launched a consultation on technology risks in the financial

sector, highlighting the need for a regulatory and supervisory framework (OSFI, 2020).

This highlights the growing interest among regulators in monitoring the impact of digital

technology on service provision. However, the managers in the developing countries

Page 28 of 49International Journal of Bank Marketing

expressed certain reservations about the role of the regulators and how this presents

certain practical implications, both for the industry and the consumers.

While this conceptual framework illustrates the key stakeholders, perhaps more

importantly, it challenges three existing notions about AI and financial services

marketing.

First, there is a distinction between AI and financial services and the use of AI in

marketing financial services. Here, AI can be applied in developing financial services and

products, but deeper insight can be used in developing marketing strategies for financial

services (See Appendix 1). The application of AI in financial services entails specific

computer knowledge and an awareness of how AI can be used for developing financial

services. However, this is often not what financial service marketing managers are

expected to do. In fact, AI and financial services involve the use of Fintech, which is often

focused on blockchain and cryptocurrencies, and while these are essentially part of

financial services, they are decidedly distinct from the marketing aspect of those services.

In short, they place a premium on ‘marketing’ the ‘financial products’ using huge

computing power, access to Big Data, and the analytical capacity of AI to raise awareness

about financial products and to make them available and accessible for all prospective

customers.

Second, the adoption of AI in financial services encompasses more than simply chatbots.

Often, the focus of the AI in the area of financial services marketing has been on providing

chatbots and facilitating customers’ engagement with them (Abduladri et al., 2021;

Mogaji et al., 2021; Riikkinen et al., 2018). However, while this is undoubtedly important

Page 29 of 49 International Journal of Bank Marketing

for service delivery, it is crucial to recognise that a chatbot is only one of the numerous

digital tools that allow customers to interact with the bank and obtain information.

Beyond chatbots, the AI in financial services marketing entails analysing customer

behaviour, marketing the financial services to individuals who need them, and developing

better ways to provide prompt and rapid decisions on credit applications. Therefore,

practitioners and marketers must take a holistic approach to the AI used in marketing and

explore other options to enhance their business operations.

Third, our study reveals that the role of bank managers in developing AI for financial

services marketing remains somewhat limited. Following our interactions with the

managers and the insights gained from the conceptual framework, it is clearly important

to separate the job description of the banks’ marketing managers and that of the Fintech

developers. Often, the managers are involved in developing these systems by sharing their

business expectations and allowing the developers to develop ideas that can help them

actualise them. Meanwhile, the developers devise solutions that can be applied to

different business sectors. For example, a chatbot developer can develop an application

for a bank and a university. It is, however, essential to recognise the working relationship

between the bank managers and the developers. Without doubt, developers require the

involvement of the marketing managers to improve their products and their ability to meet

the business needs of banks.

Managerial implications

This study provides practical implications for managers, policymakers, and other

stakeholders. Table 3 presents a summary of managerial implications. First, banks need

to understand their business objectives, resources, and customers’ needs (de Ruyter et al.,

2018; Gökerik et al., 2018). It is important to recognise that AI has a wide variety of

Page 30 of 49International Journal of Bank Marketing

applications and that one size does not fit all. Much like its customers, each bank is

unique. The fact that every company is adopting AI and that it is becoming a business

buzzword does not mean that a company should rush into using it. It is critical for banks

to understand the different options and how they fit their specific business needs.

Second, after determining whether AI is required and for what purpose, managers should

consider the full range of development options available to them to meet their specific

business needs. Managers should reflect on the importance of assembling the right team

to develop the product, should be mindful of the key performance indicators that will be

set to gauge the success of the projects, and must consider the prospects of managing and

maintaining the algorithms integrated into their business practices. While technology

development may know no geographical background, managers should consider the pros

and cons of collaborating with the developers in their country, especially the developing

countries, or working with a global team.

Third, in selecting a team or working with partners, managers should be mindful of the

ethical implications of their working relationship. This is important in terms of the

collection, aggregation, and processing of data. As Mogaji et al. (2020) highlighted, when

dealing with digital marketing and data harvesting from vulnerable customers, managers

should be mindful of the short- and long-term implications of how the data are collected

and processed for machine learning and the subsequent development of AI algorithms. In

short, the data should be fairly and ethically collected, aggregated, and processed.

Fourth, bank marketing managers should be trained and supported in understanding the

AI used for financial services. Trained and educated managers will also have a better

Page 31 of 49 International Journal of Bank Marketing

working relationship with their teams and partners, as well as be more conversant with

the technology, allowing them to influence the design process. In addition, these

managers will be able to challenge any bad practices and ensure compliance with the

regulatory requirements.

Fifth, the relevant regulators must be involved in the development of AI for financial

service marketing. In short, it is vital to have a regulatory framework in place to guide

the design process and ensure that customers are treated fairly. Furthermore, managers

should implement measures to mitigate the risks posed by their AI-based systems (Gow,

2021). As a number of our participants in the developing countries expressed certain

concerns about the regulatory requirements and awareness of AI in their country, they

must consider regulatory advice from other governmental bodies, such as the European

Union’s ethics guidelines for trustworthy AI (EU, 2019), and from tech developers such

as Microsoft, who have provided key AI-related ethical principles pertaining to fairness,

inclusiveness, reliability and safety, transparency, privacy and security, and

accountability. In addition, AI systems should involve algorithmic accountability to

enable appropriate human direction and control (Microsoft, 2017).

Lastly, communicating the prospects of AI to consumers is also crucial. In short,

consumers should be aware of the technology and how it can be used to enhance their

banking experience. Here, Mogaji et al. (2021) and Abdulhadi et al. (2021) found that the

banks in developing countries were not doing enough to communicate the availability of

chatbots to the consumers, which has influenced their adoption. Consumers should be

made aware of the benefits of using chatbots and be reassured that the AI system is secure

in supporting their transactions. In addition, according to the existing ethics guidelines,

Page 32 of 49International Journal of Bank Marketing

consumers should be assured of appropriate data privacy and governance (EU, 2019),

should be informed about how their data are being collected and aggregated to develop

financial products and should be reassured that there exist data governance mechanisms

to support the data collection, aggregation and processing.

_____Table 3_____

Conclusion

This study was aimed at exploring managers’ perceptions of the adoption of AI in the

area of marketing financial services. With the growing prospects of AI and the digital

transformation of business practices in the financial services industry, managers must be

aware of the prospects, opportunities, and challenges associated with the AI used in the

industry. The study makes a significant theoretical contribution to the existing works on

AI and the financial services market by introducing a theoretical framework that identifies

the key constructs and stakeholders in integrating AI for financial services marketing.

Specifically, the study challenged certain existing notions about AI and financial services,

the chatbots used for financial service delivery, and the role of marketing managers in

developing AI.

As with any study, the present study is not without its limitations, and the findings should

be interpreted in light of these. A qualitative-based methodology was adopted for the

study, which involved the self-reporting of the managers, meaning the findings may not

be generalisable. Individuals with different roles and levels of knowledge of AI who

reside in different countries may have other experiences of adopting AI in their business

operations. Future studies can endeavour to adopt a quantitative approach to fully

quantify the level of bank managers’ understanding. Finally, the theoretical framework

Page 33 of 49 International Journal of Bank Marketing

developed is open to further research and validation in view of establishing whether the

constructs are valid and applicable to different business operations.

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Page 39 of 49 International Journal of Bank Marketing

Table 1 - Summary of key studies and contribution of our study

Theme

Key Study

Aim

Methodology

Key Findings

Contribution of our study

Dwivedi

(2020)

Multi-stakeholder

perspectives on the

significant opportunities

and prospects of AI in

marketing and other

business practices

Conceptual

paper/

Viewpoints

Growing access to bid data

and computing capabilities

which will shape the future

of the industry and society

at large

Davenport et

al. (2020)

Postulated how AI

would change the future

of marketing, citing the

algorithm's ability to

automate business

processes, analyses big

data, and gain insight

from customers and

transaction data.

Conceptual

paper

Proposed a

multidimensional

framework for

understanding the impact of

AI in marketing

Mogaji et al.

(2020)

Proposed the

possibilities of using AI

to personalise

emotionally appealing

Conceptual

paper

AI can be developed to

collect data from customers

(including their digital

footprints) to create a

personalised advertisement

Marketing

and AI

Mogaji et al.

(2021a)

The implications of

artificial intelligence on

the digital marketing of

financial services to

vulnerable customers

Conceptual

paper

Ethical collection of data is

essential. Effectively

targeting the customers is

also necessary. There

should be a form of human

control and empathy in the

decision-making process.

Most of these studies were conceptual

and viewpoint. Our present study

provides insight into the practitioners'

perspective of Ai adoption in marking

financial services.

Beyond conceptualising and synthesising

academic literature, the study provides

empirical insight into AI's opportunities,

prospects, and challenges in financial

services marketing.

We provided insight into different

industries and different countries to

understand the drivers

and inhibitors of a successful AI adoption

in marketing financial services.

Beyond the conceptual approach of these

papers, we provide qualitative insight

from the marketing managers.

Page 41 of 49 International Journal of Bank Marketing

Ma, L., &

Sun, B.

(2020).

Provides conceptual

insight for marketing

researchers and

practitioners to leverage

machine learning

methods for business

operations.

Conceptual

Paper

Identified several marketing

research priorities,

highlighting opportunities

for machine learning

methods to be integrated

into marketing research.

Jang et al.

(2021)

Explore the managers'

perceptions of chatbots

in South Korea

Semi-

structured

interviews

with bank

managers

Managers perceive the

chatbot services to meet

customer expectations.

Quah & Chua

(2019)

Investigates the

effectiveness of the

current use of chatbots

in Singapore.

Literature

review and

interviews

Chatbots increase the

operation of banking

services and reduce

operational costs.

Riikkinenet

al, 2018

Analysed how

insurance chatbots

support customers value

creation

Literature

Review

Chatbots constitute a new

interaction medium that can

be used to influence value

creation.

Eren (2021)

Investigates effects of

customers perceived

trust in bank's chatbots.

The focus was on

Turkish consumers.

quantitative

study, a

survey of

bank

customers

Perceived performance,

perceived trust and

corporate reputation

significantly affect

customer satisfaction with

chatbot use.

Chatbots

Abdulquadri

et al. (2021)

Explores how Nigeria

banks are developing

chatbots as a digital

transformation tool to

radically change

business models,

The Search-

Access-Test

(S-A-T)

model was

adopted to

understand

Banks are adopting

chatbots, but it is essential

to consider reassuring

consumers about the

chatbot's security and

capabilities.

Our study extends the works of Jang et al.

(2021) to explore managers' perceptions

of chatbots (and AI at large) across

different countries and financial services

providers.

Our study recognises that AI in financial

services encompasses more than chatbots.

While chatbot is essential for service

delivery, it is crucial to remember that it

is just one of the numerous digital tools

that allow customers to interact with the

bank and obtain information.

While previous studies have often

focused on a particular country, our study

has a holistic view of digital

transformation across the industry and

different countries.

Our study revealed that though bank

managers are interested in having

chatbots for enhancing financial services

provision, they lack control due to the

developers' inherent design and

Page 42 of 49International Journal of Bank Marketing

improve customer

experience and enhance

financial inclusion in

emerging markets.

how

Nigerian

banks are

adopting

chatbots.

Mogaji et al.

(2021b)

Explores how

consumers in an

emerging market

interact with banking

chatbots

Qualitative

study. Semi-

structured

interview

with bank

customers

Consumers are interested in

chatbots, but they want a

responsive chatbot with a

beautiful interface and

secured features.

development of the algorithms.

Zhang et al.,

(2020).

Investigates differences

in consumers'

perceptions of trust,

performance between

humans, financial

advisors and robo-

advisors. The focus was

on consumers in the

United States

Experiments

Consumers prefer human,

financial services to robo-

advisors.

Klaus &

Zaichkowsky

(2020)

Documenting how AI

has transformed

customer decision

making.

Conceptual

paper/

Literature

Review

Consumers focus on

convenience, time saving

and effectiveness.

AI in

Financial

Services

Raj et al.

(2011)

Analysis of techniques

used in credit card fraud

detection.

Conceptual

paper/

Literature

Review

Modern credit card fraud

detection technologies are

based on AI, machine

learning, genetic

programming and sequence

alignment.

While studies are often conceptual, based

on secondary data and literature review,

while still focusing on one country, our

study provides empirical insight through

qualitative data collected from managers

and financial service providers across

different countries.

Our study presents a conceptual

framework of AI in financial services

marketing. The framework identifies key

constructs and stakeholders in adopting

AI for financial services marketing.

Our study presents a distinction between

AI and financial services and AI in

marketing financial services. AI can be

applied in developing financial services

and products, but also, a more profound

Page 43 of 49 International Journal of Bank Marketing

Bao et al.

(2020)

Review challenges in

detecting fraud using

machine learning.

Conceptual

paper/

Literature

Review

Machine learning is a

promising intervention for

fraud detection. Huge

prospect for continued use

in financial services

provision.

Kelley et al.

(2018).

Analyses the impact of

AI on the workforce in

the insurance industry

in the United States.

Conceptual

paper/

Literature

Review

AI will be a game-changer

for industries to transform

how the insurance industry

works.

Kruse et al.

(2020)

Analyses the challenges

for AI in the financial

services industry in

Germany.

Conceptual

paper/

Literature

Review

The adoption of AI faces

numerous challenges

associated with critical

success factors.

Wall (2018)

Analyses the financial

regulatory implications

of AI in the United

States.

Conceptual

paper/

Literature

Review

The rapid development of

AI will transform the

financial services sector.

insight can be used in developing

marketing strategies for financial

services.

We found that the role of bank managers

in developing AI for financial services

marketing is still limited. Our study posits

that it is essential to separate the job

descriptions of banks' marketing

managers and Fintech developers

Given that financial services have always

been a regulated industry and with AI

bringing additional concerns about

monitoring the practices of the financial

services providers. Our study reiterates

the role of the regulators, primarily as

they serve as control measures between

the engagement of the consumers and the

banks. There are also implications for

regulators in developing countries.

Page 44 of 49International Journal of Bank Marketing

Table 2. Demographic information of informants

Characteristic

Subgroup

Frequency

Male

57.4%

Female

40.4%

Gender

Non-binary

2.1%

20- 29

19.1%

30-39

36.2%

40-49

29.8%

50-59

10.6%

Age

60+

4.3%

Canada

21.3%

Nigeria

23.4%

United Kingdom

29.8%

Country

Vietnam

25.5%

Non-retail bank

40.4%

Islamic bank

4.3%

Fintech/NeoBank

25.5%

Credit card

10.6%

Industry

Insurance

19.1%

0-9

31.9%

10-19

53.2%

Years of

experiences

20 +

14.9%

Page 45 of 49 International Journal of Bank Marketing

Table 3: Summary of Findings and Managerial Implications

Key themes and Implications

Sub Themes

Sample Quotes

The Hype

The misconceptions

The Chatbot.

Awareness about AI

Managerial Implications

Bank marketing managers

should be trained and

supported in understanding

the AI used for financial

services.

Trained and educated

managers will also have a

better working relationship

with their teams and

partners

Managers should

communicate the prospects

of AI to consumers.

Consumers should be aware

of the technology and how

it can be used to enhance

their banking experience.

Consumers should be made

aware of the benefits of

using chatbots and be

reassured that the AI system

is secure in supporting their

transactions.

Consumers should be

assured of appropriate data

privacy and governance

Consumers should be

informed about how their

data are being collected and

aggregated to develop

financial products

Consumers should be

reassured that there exist

data governance

mechanisms to support the

data collection, aggregation,

and processing.

The huge possibilities

[AI is] a buzzword that everyone is

using and talking about, but few

people know what it is and how it

actually works. Everyone has a

different understanding of AI

depending on their level of

experience and exposure.

It is very broad and involves many

technicalities that can be very

confusing to people and that’s why

people keep confusing AI with

machine learning and deep

learning. There are a lot of

misconceptions out there.

…just like any technology, people

struggle to understand it and we

all learn on the job; likewise with

this AI, many people don’t know

about it yet.

[w]e all hear about it, we read

about it, and we see how

companies are using it. We all

know this is important but not very

many people know much about it

and how to go about it. Often, you

will need the tech guys to sort

something out for you.

[c]onsumers don’t know how many

algorithms are involved in

deciding if they will get a loan or

even the interest they will be

offered; they do not know much

about the influence of these

systems in deciding which type of

product they are offered; a chatbot

is just a part of the system and

there are many other hidden

values of AI not recognised by the

customers - UK retail bank.

Many customers are not aware of

the AI used in financial services

and not to mention chatbot. There

Page 46 of 49International Journal of Bank Marketing

are many things going on with

technology in the banking sector –

Nigerian Bank Manager

Addressing AI as a

Business Need

Evolving naturally

Convincing the

management team

Assembling the right

Deploying the systems

Addressing the Business Need

Managerial Implications

Banks need to understand

their business objectives,

their resources and their

customers’ needs

Managers should reflect on

the importance of

assembling the right team to

develop the product

Managers should consider

the pros and cons of

collaborating with the

developers in their country,

especially the developing

countries, or working with a

global team.

Managers should be mindful

of the ethical implications of

their working relationship

with developers and other

suppliers.

This is important in terms of

the collection, aggregation,

and processing of data.

Managers should be mindful

of the short- and long-term

implications of how the data

are collected and processed

for machine learning and the

subsequent development of

AI algorithms.

Managing the team

[h]ighlighting how our competitor

is using AI often works in terms of

convincing the management team

that we should do the same. You

must recognise that no bank wants

to lose out due to this digital

transformation – Vietnamese

Bank Manager

[o]ne of these Fintech companies

pitched an idea for us. We thought

it was nice and the team liked it as

it would complement what we

already had, but the pricing

structure was not making much

financial sense; we had to see if we

could do the same in-house with

our team, but we found that it was

not feasible, so we finally agreed

to get another tech company to

work with us – UK Manager

I must acknowledge my weakness

at this point with regard to this

technology. I was on the team to

make sure we integrated the AI

system for credit scoring and

decision-making, but it was not a

good experience. We had the idea

of what we wanted to do, but the

team couldn’t just come up with

something worthwhile. I tried to

engage with some of the

terminologies and technicalities,

but it seems we were on a different

threshold – Nigerian Bank

Manager.

The Regulator’s role

The consumers’ trust

The managers

knowledge

The manpower

Accelerating AI adoption

Managerial Implications

Managers should consider

the full range of

development options

The country of

operations

[c]onsumers need to be assured

that we are working to develop a

system that works for them and for

the banks. Consumers should trust

the bank in that the decisions being

made through the system are right

and unbiased – Canadian Manager

Page 47 of 49 International Journal of Bank Marketing

available to them to meet

their specific business

needs.

Managers should ensure that

data are fairly and ethically

collected, aggregated and

processed.

Trained and educated

managers will be more

conversant with the

technology,

Managers will be able to

challenge any bad practices

and ensure compliance with

the regulatory requirements.

Relevant regulators must be

involved in the development

of AI for financial service

marketing. Regulatory

framework should be in

place to guide the design

process and ensure that

customers are treated fairly.

Managers should implement

measures to mitigate the

risks posed by their AI-

based systems

Managers must consider

regulatory advice from other

governmental bodies.

AI systems should involve

algorithmic accountability

to enable appropriate human

direction and control

[t]he use of AI in financial services

will become more advanced, going

beyond chatbots and credit scoring

and fraud protection; consumers

should be open-minded to its

benefits – UK Manager.

[m]y basic understanding of

python as a programming

language relates to how it helps me

in having a conversation with the

team; I think marketers need to

acquire more of this technical

knowledge, with even the

management team needing to

understand the basics of AI in

business operations - – UK

Manager

[a]s part of my own professional

development, I am doing a short

course online to have a better

understanding of AI. This is a

computer course, but I need it. I

am learning about blockchain,

machine learning and deep

learning, trying to make sense of it

-Vietnam Manager

Page 48 of 49International Journal of Bank Marketing

Appendix 1: Distinction between AI and financial services and the use

of AI in marketing financial services

Page 49 of 49 International Journal of Bank Marketing

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Oct 2021
IND MARKET MANAG

The new business challenges in the B2B sector are determined by connected ecosystems, where data-driven decision making is crucial for successful strategies. At the same time, the use of digital marketing as a communication and sales channel has led to the need and use of Customer Relationship Management (CRM) systems to correctly manage company information. The understanding of B2B traditional Marketing strategies that use CRMs that work with Artificial Intelligence (AI) has been studied, however, research focused on the understanding and application of these technologies in B2B digital marketing is scarce. To cover this gap in the literature, this study develops a literature review on the main academic contributions in this area. To visualize the outcomes of the literature review, the results are then analyzed using a statistical approach known as Multiple Correspondence Analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. The research results classify the types of CRMs and their typologies and explore the main techniques and uses of AI-based CRMs in B2B digital marketing. In addition, a discussion, directions and propositions for future research are presented.

Developing corporate social responsibility in financial services

Article

Full-text available

May 2021
Int J Bank Market

Purpose The purpose of this study is to contribute to the theory and practice of financial services marketing in sub-Saharan Africa (sSA) by investigating how financial service providers are developing corporate social responsibility (CSR) practices, in particular, seeking to uncover the involvement of stakeholders. Design/methodology/approach Following an interpretivist approach, the study uncovers fresh and context-rich insights through an analysis of a multiple case study consisting of retail banks in Ghana. Data consist of semi-structured interviews with senior managers and analysis of documents and archives. Findings The study uncovers three key CSR practices practised by the retail banks: giving, community and corporate reputation/brand with which their stakeholders are only to some extent involved. Banks not as yet drawing extensively on stakeholder resources for CSR practices. Research limitations/implications The study uses an inductive and in-depth approach to explore contextual insights into CSR, but with subsequent limitations on how far the findings can be extended. Practical implications The study offers outline for financial services marketing involving stakeholders in CSR. Social implications It discovers that banks acquire social capital through their CSR activities in the community. Originality/value The study contributes to financial services marketing theory and practice through an evidence-based framework uncovering the development of CSR through practices that as yet draw on stakeholder resources to a limited extent. Research suggests that CSR practices are dynamic and subject to a range of situational conditions.

Marketing and Financial Services in the Age of Artificial Intelligence

Chapter

Full-text available

Apr 2021

Marketing is changing rapidly with the advancements in AI systems. AI presents several opportunities as gaining insight, hyper-personalization, enhancing the customer experience, providing customers with better service, reducing operational costs, increase efficiency, etc. Artificial intelligence has become a game changer for marketers as it is for financial services providers. Therefore, it is paramount to build a good understanding of AI in the marketing context and mention the fundamentals of AI usage in financial services as AI is introduced as a significant competitive edge factor in financial marketing in recent years. This study aims to explore AI and marketing in a theoretical base and aims to explain the issue with a holistic approach.

Marketing bank services to financially vulnerable customers: Evidence from an emerging economy

Article

Full-text available

Feb 2021
Int J Bank Market

Purpose: This study aims to explore how banks in Nigeria are marketing financial services to financially vulnerable customers. Design/methodology/approach: A multiple case study research strategy was used to analyse three commercial banks and two microfinance banks. Data was collected using semi-structured interviews with the banks' directors, as well as from banks' published annual reports and archival images. Findings: The study reveals that Nigerian banks develop different product development portfolios, adopt innovative traditional marketing schemes, and apply inclusive technologies to reach and extend services to the unbanked and financially vulnerable customers in the society. Originality: This study shows how financial services operators' market and extend their services to financially vulnerable customers in emerging markets. It empirically establishes the importance of financial services to financially excluded customers. Research implications: Banks should focus on consumer engagement through the proactive development of technologies and employ innovative marketing methods. Customers' banking experiences can be enhanced if banks communicate with and educate customers about technological modes of engagement. In addition, financial services transactions support, and financial literacy education can assist banks in marketing their services to financially vulnerable customers; in mutually beneficial ways.

Emerging-Market Consumers’ Interactions with Banking Chatbots

Article

Sep 2021

Chatbot development and adoption is a growing challenge in emerging markets, which are characterised by a young population structure, low-level internet penetration and a high degree of institutional adversity. The purpose of this study is to explore how consumers in emerging markets interact and engage with banking chatbots when conducting bank transactions. Based on qualitative research using semi-structured interviews with 36 Nigerian residents, this study demonstrates that the four factors of the unified theory of acceptance and use of technology (UTAUT) can explain how emerging-market consumers interact and engage with banking chatbots. While age and technological experience are significant components in facilitating the use of chatbots, perceived expertise, responsiveness and security were found to be particularly important to users. This study delves into user experiences with conversational interfaces, specifically focusing on emerging markets.

Artificial Intelligence (AI): Revolutionizing Digital Marketing

Article

Aug 2021

Artificial intelligence (AI)-enabled digital marketing is revolutionizing the way organizations create content for campaigns, generate leads, reduce customer acquisition costs, manage customer experiences, market themselves to prospective employees, and convert their reachable consumer base via social media. Real-world examples of organizations who are using AI in digital marketing abound. For example, Red Balloon and Harley Davidson used AI to automate their digital advertising campaigns. However, we are early in the process of both the practical application of AI by firms broadly and by their marketing functions in particular. One could argue that we are even earlier in the research process of conceptualizing, theorizing, and researching the use and impact of AI. Importantly, as with most technologies of significant potential, the application of AI in marketing engenders not just practical considerations but ethical questions as well. The ability of AI to automate activities, that in the past people did, also raises the issue of whether marketing professionals will embrace AI as a means to free them from more mundane tasks to spend time on higher value activities, or will they view AI as a threat to their employment? Given the nascent nature of research on AI at this point, the full capabilities and limitations of AI in marketing are unknown. This special edition takes an important step in illuminating both what we know and what we yet need to research.

Financial services experience and consumption in Nigeria

Article

Jul 2021
J Serv Market

Purpose To understand the financial services experience and consumption in Nigeria from the perspectives of both the customers and managers. This study aims to explore this under-researched area and contribute towards a transformative financial service in the country. Design/methodology/approach Semi-structured interviews with 26 bank customers and seven top bank executives. Findings A conceptual framework, which has emerged from the analysis of the results, revealed three overarching factors that shape these experiences from the perspective of both consumers and managers – service maintenance, service technology and service dynamics. Practical implications For sustainable service maintenance, bank executives need to increase the overall level of transparency in their operations, particularly regarding bank charges, to ensure that customers are not subjected to hidden and unnecessary charges. The use of technology in service provision and delivery should play a prominent role. Managers should also provide innovative and user-friendly technology, communicating with customers and raising awareness of the benefits. Customers who are reluctant to adopt the technology should be educated and reassured. Recognising the service dynamics, managers should improve customer services and relationships, effectively manage the mobile money agent relationship and market new and relevant products to their target audience. Social implications The understanding of the financial services experience and consumption of citizens and residents in the demonstrate how the appropriate programmes and policies that enhance financial inclusion could be introduced and implemented in the country. It enables financial service managers to improve their services to their customers and policymakers to develop timely, relevant and appropriate policies to address and/or bridge the identified gaps in financial inclusion. The understanding of the financial services experience and consumption of citizens and residents in the demonstrate how the appropriate programmes and policies that enhance financial inclusion could be introduced and implemented in the country. Originality/value Through the sampling, this paper reiterates the need for consumer engagement and collaborative customer-provider relationships in redesigning financial services. This aligns with the transformative research agenda, which aims to increase access to financial services, decrease disparity and ensure consumers’ financial well-being.

Digital transformation in financial services provision: a Nigerian perspective to the adoption of chatbot

Article

May 2021

Purpose Recognising the high numbers of unbanked and financially excluded adults in Nigeria, this study aims to position chatbot as a digital transformation tool to radically change business model, improve customer experience and enhance financial inclusion in emerging markets. Design/methodology/approach The Search-Access-Test (S-A-T) model was adopted to understand how Nigerian banks are adopting chatbots. Findings A majority of Nigerian banks now have chatbots that enhance customer engagement and financial inclusion. WhatsApp was the most frequently used platform. Chatbots were often branded and presented with female gender identification. The chatbots were less responsive beyond their predefined path. While Nigeria is a multilingual country with English being the original language, none of the chatbots used any of the Nigerian’s local languages. Practical implications Brands need to re-evaluate their chatbots with regard to responsiveness, predefined questions, verification and privacy. There are also possibilities of branding the chatbot and developing content creation strategies for proper engagement. Beyond English, the integration of African languages into chatbot is essential for digital transformation. Digital literacy and skills, particularly in the field of science, technology, engineering and mathematics, should be supported to equip future developers and create more jobs. Originality/value While many theoretically based models for investigating the adoption of digital technologies have often placed focus on users’ ability to engage, this study takes an alternative perspective; by using the S-A-T model, it lays the responsibilities on the banks and chatbot developer to ensure that their chatbots are secure, responsive and able to meet the needs of the customers.

Building blocks of financial inclusion through customer experience value at financial touchpoints in Southern Africa

Article

Apr 2021
Int J Bank Market

Purpose This paper aims to examine customer experience value orchestrated by non-banks' financial touchpoints to understand how they enhance the financial inclusion of low-income consumers. Design/methodology/approach Two independent but related studies were conducted using qualitative comparative analyses (QCA) research design with semi-structured interviews to compare and contrast customer experience value at two rural locations in Southern Africa. The interview transcripts were analysed using ATLAS.ti, which is a powerful operating system for analysing qualitative data. Findings The results indicate that non-banks in the two countries design financial services that include functional, economic, humanic, social and mechanic customer experience value dimensions. Research limitations/implications The data for this study was collected from financial services customers of retailers and mobile phone network operators in only one research setting in each country. Further research could extend the comparative context for qualitative studies across similar markets. Other limitations are discussed in the paper. Originality/value This paper contributes to the body of knowledge by highlighting the salient and germane dimensions and components found to be important in understanding financial inclusion using customer experience value. To the best of the authors’ knowledge, this is the first study that incorporates customer experience value dimensions in understanding the financial inclusion of low-income consumers at the base of the social and economic pyramid in emerging markets.

Investigating Managers’ Understanding of Chatbots in the Korean Financial Industry

Article

Feb 2021
COMPUT HUM BEHAV

There is growing interest in chatbots as a leading technology for digital transformation in the financial industry. However, studies have largely investigated chatbots from the perspective of customers and developers, and very few examine chatbot services from the perspective of managers, who play a central role in the organizational adoption of chatbots. This paper aims to explore managers’ perception of chatbots to help understand the initiation of financial chatbots and forecast the future of their service in South Korea, which has seen active use of chatbots by financial firms. We conducted semi-structured interviews with managers in charge of chatbot services in Korean financial firms and employed a core–periphery analysis of social representations. Our findings will enhance the understanding of the status of chatbot services in the Korean financial industry and provide theoretical implications and managerial insights.

Managers' Understanding of Artificial Intelligence in Relation to Marketing Financial Services: Insights from a Cross-Country Study

Abstract

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