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A Critical Review of Applications of Artificial Intelligence (AI) and its Powered Technologies in the Financial Industry

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Gangu Naidu Mandala
Gangu Naidu Mandala
Gangu Naidu Mandala
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Dharam Buddhi
Dharam Buddhi
Dharam Buddhi
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Mahalakshmi Arumugam at M.S. Ramaiah Institute of Technology
Mahalakshmi Arumugam
Suhas Harbola
Suhas Harbola
Suhas Harbola
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A Critical Review of Applications of Artificial
Intelligence (AI) and its Powered Technologies in
the Financial Industry
Gangu Naidu Mandala1
1
Department of Professional Studies, CHRIST
Deemed to be University, Bengaluru
dr.gnmandala@gmail.com
Mahalakshmi Arumugam2*
2Associate Professor ,
Department of Management Studies,
M S Ramaiah Institute of Technology,
Bangalore 560 064
mahalakshmi.a@msrit.edu
0000-0003-4567-6138
Bestoon Othman3
3
Business Administration, Koya Technical
Institute, Erbil Polytechnic University, Erbil,
Iraq.
Department of Business Administration, College
of Administration and Economics, Nawroz
University, Duhok, Iraq.
Bestoon2011@yahoo.com
Dharam Buddhi4
4
Professor, UIT, Uttaranchal University,
Dehradun, Uttarakhand, India.
dbuddhi@gmail.com
Suhas Harbola5
5
National Informatics Centre, New
Delhi, India
suhas.harbola@gmail.com
https://orcid.org/0000
-0003-3586-
0337
Hashem Ali Almashaqbeh6
6Assistant Professor, Qatar Universi
ty, Doha,
Qatar
hashem61994@gmail.com
https://orcid.org/0000
-0002-5838-8031
Abstract: The present research shed light on the
applications of AI technologies for the financial industry of the
UK. The research has also investigated the different types of
powered technologies of AI and their impact on finance
operations and activities. This research possesses the tools and
techniques used by the researcher in gathering the research
evidence for the proper completion of the research work.
Keywords: AI technology, financial industry, powered
technologies
I. INTRODUCTION
Artificial intelligence is the ability of the computer,
through which different business activities like data
management and financial activities can be managed
efficiently. The most advantageous part of the AI technology
is that it can learn new things automatically by developing
patterns from existing data. Moreover, based on that pattern
it also able to analyse the risk factors of the business. The
application of AI technology is quite important for the
finance industry since in the finance industry all the
companies needs to deal with numerous sensitive data that
must be handle and stored securely [1]. In order to
understand this factor in depth, the present study portrays the
important applications of AI technology, which can manage
financial tasks and work for financial companies with much
ease. Moreover, a detailed investigation about the present
research was important for the researcher to gather
appropriate evidence about the powered technologies of AI,
which is useful to predict cash flows, detects frauds and
adjust credit scores [2]. In addition, the present research also
portrays the several data collection tools and approaches
adopted by the researcher to collect appropriate research
evidence, which could be helpful for him in the detailed
investigation of AI technology and its powered technologies.
II. LITERATURE REVIEW
Artificial intelligence is one of the most trending and
useful technology in the finance sector for managing the
statistical measurements about the monetary variations and
trend analysis. It is quite evident that the role of the finance
industry of the UK is to manage the movement of cash and
keep balance the liquidity requirements of the industry [3].
This can be done by managing the customer's expectations
and identifying their savings and investment level for a
specific period of time. Hence, the use of AI-powered
technologies is very helpful for a finance firm to manage
their daily records and transactions. The continuous
increment of the data and transaction history from huge
population is quite difficult things to manage by using the
typical manual process [4]. Due to such kind of activities,
numerous mistakes can be take place. In order to prevent
such errors in calculations, AI technology has been
introduced with its powered algorithm. Starting from the risk
management, fraud detection and prevention to credit
decision and financial advisory, everywhere the application
of AI is undeniable. Artificial intelligence can be analyse the
spending patterns of the customers and their regular financial
activities based upon which loan borrowing behaviour can be
predict [5]. For example, when a loan applicant download an
app in smart phone, the AI based lender would use to analyse
the digital footprint of that user like social media use,
browsing history, credit card statement, text message reading
and more in order to build a more complete picture.
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2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) | 978-1-6654-3789-9/22/$31.00 ©2022 IEEE | DOI: 10.1109/ICACITE53722.2022.9823776
Authorized licensed use limited to: M S RAMAIAH INSTITUTE OF TECHNOLOGY. Downloaded on August 16,2022 at 08:02:06 UTC from IEEE Xplore. Restrictions apply.
Fig 1: AI applications
(Source: [5])
The powered technologies of AI could become vital in
different aspects of the financial industry. This is because it
can secure their system by eliminating the risk of human
error [6]. The technology is also providing the experience of
a personalized banking system by predicting the customer's
savings and investments. The technologies are also helpful in
the automation of work by which the financial firms can do
the stock market prediction along with the sales forecasting
[7].
Moreover, AI technology is also helpful for controlling
the market capitalization by monitoring the stock market.
Fig 2: Market Capitalization
(Source: [8])
The increasing percentage of market capitalization in the
finance industry of the UK depicts that proper controlling
and monitoring process is important for managing the inflow
and outflow of the cash and cash equivalent. Moreover, in
2020, the financial sector of the UK will contribute 164.8
billion to the UK economy, which is approximately 8.6% of
the overall contribution to the economy of the country [8].
For this reason, the controlling and monitoring of the
financial industry of the UK is important by using the
powered technologies of AI. The powered technologies of AI
can be categorized as “machine learning platforms,
decision management, robotic process, automation process,
and deep learning platforms” [9]. By utilizing these
technique it is able to scale the short time and long-time
projects with designing the efficient budget. Thus, in the
following way, the artificial intelligence helps the financial
organization to operate in a better way.
III. RESEARCH METHODOLOGY
The research methodology could play a significant role in
portraying the systematic relationship between the powered
technologies of AI and the finance industry of the UK.
Moreover, the research has been applied to several
philosophies, designs, and data collection tools for doing a
detailed analysis of the impact of AI technology on the
financial sectors [10]. Moreover, the researcher has adopted
the positivism philosophy for gathering factual knowledge
about the finance industry and the required applications of
the powered technologies for the industry. Moreover, a
descriptive research design is also used by the researcher for
eliminating the research problems in a systematic way [11].
Therefore, it is also illustrating that the data and information
about the finance industry and applications of AI
technologies have been gathered by the researcher by doing
primary as well as secondary research. The researcher has
taken the interview and survey of 70 employees of the
finance industry of the UK to gather more information about
the impact of AI on the finance industry [12].
On the other hand, secondary research has helped in the
collection of reliable data about the contribution of the
finance industry to the economy of the country. However,
the researcher has not possessed enough time to complete the
research which was one of the concerns for the researcher
and in the limited time, he has to complete the work by
following all the ethics of the research [13]. Besides, the
research implications have put all his efforts into the
completion of the research accurately and efficiently.
IV. ANALYSIS AND INTERPRETATION
Primary research analysis
Fig 3: Response of sample Q1
(Source: Created by the researcher)
Explanation
From the computation of the above table and graph, it is
observed that AI technology has provided a positive impact
on the finance industry of the UK. This is because
approximately 57% and 14% of employees have thought that
the technologies of AI have changed the way of doing
financial work. For this reason, it is stated that AI has played
a significant role in the finance industry for the improvement
of their performance in the UK market.
AI in
Finance
Sales
forecasting
Trading
Personalis
ed Banking
Risk
manageme
nt
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Fig 4: Response of sample Q2
(Source: Created by the researcher)
Explanation
According to the above graph and table, most employees
stated that the powered technologies of AI have enhanced the
growth process of the finance industry by starting the
automation process. Approximately 29% and 36% of
employees strongly agree and agree on the question
however; only 10% and 4% of employees disagree on the
questions. Hence, according to the majority, it can be
observed that the technologies of AI have eliminated the risk
of human error and it positively increases the growth process
of the industry.
What is most important to create effective powered AI
technologies for the investigation of the finance industry?
TABLE III: RESPONSE OF QUESTION 3
(SOURCE: CREATED BY THE RESEARCHERS)
Google form options
Total
participant
s
Collected
response
s
Application of
relevant models
70
50
Evaluation of
different sized
information
70
15
Evaluation of low
data with more
number of models
70
5
Fig 5: Response of sample Q3
(Source: Created by the researcher)
Explanation
Approximately 71% of employees have the thought that
application of relevant models could be helpful in the
creation of effective powered technologies of AI. This means
considering the relevant models is important for the
researcher while collecting the information and data about
the finance industry of the UK.
Secondary research analysis
The secondary research analysis is as important as the
primary research. Besides, it shows the relevant
information about the growth in the finance industry
rapidly [1]. By using the online articles and journals
the research could collect vital information about the
application of AI as well as the performance of the
financial industry of the UK. The use of the internet could
also be helpful for the research in appropriate and
accurate completion of the research by doping the in-
depth investigation of the finance sector [1]. Based on the
published articles it has been found that the AI based
technology is also helps the organization by enabling 24x7
customer interaction. It is to be noted that money is such
an essential thing that can be required any time of the
day. Therefore, restriction of the money transaction
only within office hours is not only demote the
customer satisfaction but also may create challenges during
emergency situation. By considering this factor, AI
brought automated computerized digital transaction
process where individuals can get the financial services at
the anytime [1]. Moreover, in case any issues arise
related to financial transaction or relevant to it, AI also
offers chat-bots and virtual assistants to provide the
customer support at any time.
V.DISCUSSIONS AND FINDINGS
It is found from the above discussion that the
applications of AI technology have a significant impact
not only on the finance sector but also help to develop
the overall data management system [1]. The finance
industry of the UK has mostly availed the benefits of AI
technologies in their work since due to automation human
work has reduced and it reduced the risk of human error as
well. Moreover, it is also found that monitoring the financial
transactions with the use of technology is important for their
security since this sector contributes a significant part to
the UK economy [1]. Several powered technologies of
AI have also been identified in the above discussions
that are “machine learning platforms, decision
management, robotic process, automation process,
and deep learning platforms”. Therefore, by applying
the powered technologies of AI the finance industry is
growing at a consistent rate and by seeing the responses of
the employees for the finance industry it is assumed that
the industry will grow more in the future as well [1].
Based on the above study it also has been found that
AI also helps to reduce the repetitive mundane that usually
need to done in the financial sectors. In addition to
this, the combination of the machine learning and the
artificial intelligence effectively makes the time
consuming works faster by using same data to fill-up the
similar blank boxes []. Moreover, starting from the
reviewing documents to pulling information from the
applications. Everywhere the usage and advantages of AI is
undeniable.
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Data protection is another most concerning factors for the
financial industries as it works with numerous sensitive data
like account details, user ID of internet baking, password
and so on that must be stored safely []. AI based
powered technology offers cybersecurity based
encrypted environment through which the user can easily
transact with full of safety. In order to turn this concept into
reality, the AI based algorithm boost company security by
analysing and determining the pattern of the normal data
and trends that effectively altering companies in an
immediate basis when discrepancies and unusual activities
detects []. Thus in the following way, 95% of the cloud
breaches can be prevent by reducing human errors.
VI.CONCLUSION
It is concluded from the above discussions that the
financial industry has grown continuously due to the use of
powered technologies of AI. It is also investigated in the
research that the financial industry contributes a large part of
income in the UK economy for which maintaining the safety
and reliability of financial transactions is important for the
industry. Most financial companies of the UK are using AI
technology for their business for secure the business and
maintaining the financial stability of the industry by
estimating their required liquidity. Lastly, it is concluded that
without the use of AI technology it would not be possible for
the financial firms to operate their business with much ease
and for this, there is a significant role of AI in the higher
growth and performance of the financial industry.
VII.ACKNOWLEDGEMENT
I would like to show my special gratitude to my entire
teachers and colleagues for their help in this research since
without their help the completion of this research work could
not be possible.
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Developments in artificial intelligence technology have also had an impact on various sectors. One of the sectors where artificial intelligence technology is most widely used is finance. This fact arouses the interest of researchers, and the literature on applications of artificial intelligence in finance continues to grow. Therefore, the aim of this study is to examine the evolving literature on artificial intelligence and expert systems in finance. The bibliometric analysis approach was used to evaluate 452 articles published in the Scopus database between 1988-2022. Analyzes by country, university, journal, and author were performed using the R-based bibliometrix program. As a result of the study, it was found that although the number of articles has increased over the years, the largest increase occurred in recent years. The most productive and impactful journal is “Expert Systems with Applications”, and the most impactful author is Doumpos (2001). However, the institution and country with the highest number of publications are “Hunan University of Finance and Economics” and China, respectively. Moreover, China is the country with the most interactions. On the other hand, it was found that the most frequent keyword in the studied papers is artificial intelligence and that this concept has a strong connection with the concepts of finance and machine learning. The concept of expert systems ranks sixth in terms of the number of uses. The results of this study provide an overview of the literature on artificial intelligence and expert systems in finance.
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INVESTIGATING THE IMPACT OF ARTIFICIAL INTELLIGENCE TOOLS IN FINANCE
Thesis
  • Jun 2023
The integration of artificial intelligence (AI) solutions in financial institutions has yielded substantial improvements in diverse domains, including decision-making, risk assessment, fraud detection, and customer service, among others. The implementation of AI has the capacity to considerably augment financial analysis, prognostication, and overall efficacy. However, extant literature lacks sufficient investigation to explicate the other pertinent studies. The objective of this investigation is to examine the utilization of artificial intelligence techniques within the financial services industry. The study was conducted by means of a systematic literature review in accordance with the PRISMA diagram guidelines. This study employed a systematic literature review approach, utilizing various academic databases including Science Direct, IEEE, EBSCO, SCOPUS, and Web of Science. The search was conducted using selected keywords within the timeframe of 2019 to 2023. The study's primary findings offer a synopsis of the increasing attention towards the implementation of AI technologies in the financial sector. The systematic literature review showed that AI tools have received noteworthy attention for their capacity to revolutionize diverse facets of the industry, encompassing but not limited to decision-making, risk evaluation, fraud identification, customer service, investment guidance, and customized banking. In addition, the amalgamation of deep learning techniques and artificial intelligence has exhibited the capability to mitigate cognitive and affective errors, leading to enhanced financial gains for banking organizations and increased satisfaction among their customers. This study aims to provide guidance to stakeholders in the finance sector regarding the integration of artificial intelligence tools into their operations. Keywords: Artificial Intelligence, tools, finance, machine learning, fintech
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Navigating the decision‐making landscape of AI in risk finance: Techno‐accountability unveiled
Article
  • May 2024
The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data‐centric decision‐making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno‐stressors) impact both techno‐eustress (positive stress) and techno‐distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e‐survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno‐accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI‐driven innovations.
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Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review
Article
Full-text available
  • Jan 2022
  • J MED INTERNET RES
Background Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. Objective This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. Methods A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted. Results Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study. Conclusions The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.
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Medical Applications of Artificial Intelligence (Legal Aspects and Future Prospects)
Article
Full-text available
  • Dec 2021
Background: Cutting-edge digital technologies are being actively introduced into healthcare. The recent successful efforts of artificial intelligence in diagnosing, predicting and studying diseases, as well as in surgical assisting demonstrate its high efficiency. The AI’s ability to promptly take decisions and learn independently has motivated large corporations to focus on its development and gradual introduction into everyday life. Legal aspects of medical activities are of particular importance, yet the legal regulation of AI’s performance in healthcare is still in its infancy. The state is to a considerable extent responsible for the formation of a legal regime that would meet the needs of modern society (digital society). Objective: This study aims to determine the possible modes of AI’s functioning, to identify the participants in medical-legal relations, to define the legal personality of AI and circumscribe the scope of its competencies. Of importance is the issue of determining the grounds for imposing legal liability on persons responsible for the performance of an AI system. Results: The present study identifies the prospects for a legal assessment of AI applications in medicine. The article reviews the sources of legal regulation of AI, including the unique sources of law sanctioned by the state. Particular focus is placed on medical-legal customs and medical practices. Conclusions: The presented analysis has allowed formulating the approaches to the legal regulation of AI in healthcare.
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Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics
Article
Full-text available
  • Sep 2021
The agri-food sector is an endless source of expansion for nourishing a vast population, but there is a considerable need to develop high-standard procedures through intelligent and innovative technologies, such as artificial intelligence (AI) and big data. This paper addresses the research concerning AI and big data analytics in the food industry, including machine learning, artificial neural networks (ANNs), and various algorithms. Logistics, supply chain, marketing, and production patterns are covered along with food sub-sector applications for artificial intelligence techniques. It is found that utilization of AI techniques and the intelligent optimization algorithm also leads to significant process and production management. Thus, digital technologies are a boon for the food industry, where AI and big data have enabled us to achieve optimum results in realtime.
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ABC Technology -Artificial Intelligence, Blockchain Technology, Cloud Technology for Banking Sector
Article
Full-text available
  • Dec 2020
Banking sectors plays a critical role in modern society and enables a range of applications from infrastructure to social media. In this study, we discuss the current status and future directions as three emerging key technologies (Artificial Intelligence, Block chain, Cloud computing) will influence future need of banking sectors. Artificial intelligence in machines will replace to increase human capabilities. AI is the intelligent machine and software used to communicate with human for the purpose. Block chain provides data privacy and security. Cloud computing is a new technology. It allows client to access their personal files using internet access and using without any application. It enables sharing of resources to reduce execution cost and increase availability of service. Finally, we proposed a conceptual model to explore the influence of emerging paradigms and technologies on evolution of banking sectors.
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An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications
Article
Full-text available
  • Sep 2020
The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In
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Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-based Transformation Projects
Article
Full-text available
  • Mar 2020
  • Bus Process Manag J
The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (i) analysis of AI and AI concepts/technologies; (ii) in-depth exploration of case studies from a great number of industrial sectors; (iii) data collection from the databases (websites) of AI-based solution providers; and (iv) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations. This study has called on the theory on IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase. AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can therefore enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes. AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared towards a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations and, at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships, and scalable infrastructure. This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 cases studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.
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3d Printing: Basic principals and applications
Article
  • Jun 2021
Due to the economic growth and worldwide interference with today’s globalization and industrialization, the manufacturing sector has gained greater prominence in the last decade. Many fresh revolutionary tactics have been created, and revolution is an endless narrative. Recent popularity for 3D and additive printing concerns established norms worldwide. In contrast, the manufacture rate, quality and efficiency of existing systems are greatly enhanced. This recurrent technological transformation pushes firms to adopt these new technologies to their production methods. Most manufacturers are unlikely to fit into the new system because of a number of issues such as capital shortages, lack of funds, lack of knowledge, lack of professional organizations and so on. Besides with these technical advances may also play a major part in influencing the industrial industry, both ethical and reputational. In addition to consideration of supply chain risks and resilience, the recent growth in the manufacturing industry focusing on sustainability changes the way manufacturers are thinking. Multi-perspective analyzes are necessary for surviving to endure in the production environment. Contrary to this, manufacturers and shareholders have been showing in the last several years that the financial benefits not only improve production but also bring considerable returns to the business in view of the ecological and social advantages. In recent years, manufacturers have had a huge push in combination with economic, environmental and social principles to look at technical advancement.
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The impact of artificial intelligence and machine learning on the global economy and its implication for the hospitality sector in India
Article
  • Jun 2021
Purpose The purpose of this paper is to explore and evaluate the existing and future impact of artificial intelligence (AI) and machine learning on the global economy. It includes viewing the inclusion of AI in different sectors, its impact on industries, the trends of the forerunning companies that are capitalizing on AI and the idea of crystalizing exponential growth while maintaining a balance between the understanding of humans and the subsequent possibilities of AI. Design/methodology/approach This paper is based on secondary research, reviewing literature based on different industries and perspectives. Findings The global potential of AI is exponential; the development of AI should be effective. Globally, we see contrasting views, defining the consequences of AI. Hence, the balance between humans and AI, protocols and a global regulatory system needs to be established to prevent catastrophic results soon. Practical implications The benefits of AI are enormous. The rising incorporation of AI must take into consideration the basic safety fundamentals for a better future. Social implications This paper will enable readers to understand the importance of AI in the global economy, its current involvement in major industries and the subsequent need for balance in technology. Originality/value This conceptual review is by its nature and original contribution and, specifically, an interpretation for India.
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Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm
Article
  • Apr 2021
In the present analysis 15 experiments were performed in conjunction with the Box-Behnken architecture matrix based on the machining parameter's effect, like spindle speed, feed rate, and cutting width., A surface roughness mathematically framework was designed using the surface reaction methods of this model to aid a genetic algorithm. Which is used to decide the optimum machining parameters. Response surface methodology has been used in this paper due to certain advantages as compare to other methodology such as it needs fewer experiments to study the effects of all the factors and the optimum combination of all the variables can be revealed. Finally, a genetic algorithm was used to determine the optimum setting of process parameters that maximize the rate of content removal. The best surface roughness response value obtained from single-objective genetic algorithm optimization was 1.19 μm.
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A Conceptual Framework on Defining Businesses Strategy for Artificial Intelligence
Article
  • Sep 2020
The technological developments on artificial intelligence (AI) are going to diffuse in all scales of firms in different industries. AI is increasingly used in diverse business functions, including marketing, customer service, cost reduction, and product improvement. Although there exists a large number of studies on AI, those focusing on the businesses are rather rare, and there is no holistic conceptual framework that brings the information on defining AI business strategy. The aim of this paper is to develop a conceptual framework on defining AI business strategy through a systematic literature review (SLR) of research conducted between 2015 and 2019. Consolidating business and technical views of AI, the paper discusses the major elements of AI in business like abilities and limitations of AI, economics and AI, business functions and AI, workforce, industries and AI, and regulations and ethics of AI on defining AI business strategy.
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