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Regulatory Technology (RegTech) and Money Laundering Prevention: Exploratory Study from Bahrain
Abstract
This study aims to illustrate the impact of adopting RegTech innovations in banks on their money laundering prevention programs. The banking industry changed massively after the financial crisis of 2008. New regulations and enforcements are being imposed on banks causing the compliance cost to increase dramatically. RegTech has been invented by technology firms acting as a potential solution to banks. The study will demonstrate the ability of RegTech to reduce the compliance cost, strengthen money laundering prevention and reduce the reputational risk in banking sectors. This study target sample was banks’ employees in Bahrain because of their proper knowledge about anti-money laundering. Data were collected from 100 respondents from the banking sector of Bahrain. Descriptive analysis was used to analyze the data while regression model and Spearman’s correlation were used to test the hypothesis. The results of this analysis indicate that RegTech has positive impact on strengthening and enhancing money laundering prevention in banks. The findings of the study will help banks understand the effectiveness of RegTech solutions, raise bankers’ awareness about the new technologies and provide insight for regulators about RegTech capabilities in preventing money laundering.
... Further technological advances have opened up exciting opportunities for regulators and financial institutions seeking to streamline their AML compliance processes. Regulatory technologies leverage ML and AI to automate time-consuming tasks such as CDD, transaction monitoring, and risk assessment (Turki et al. 2021;Kurum 2020). For example, regarding CDD, ML has been used to analyze customer data to identify high-risk customers or transactions that may indicate money laundering or other financial crimes (Masciandaro and Filotto 2001;Kurum 2020). ...
... In other areas such as transaction monitoring, AI has analyzed real-time transactions to identify unusual or suspicious money laundering activity (Han et al. 2020;Kurum 2020;Ketenci et al. 2021). AI solutions in AML compliance have increased efficiency and accuracy in predicting money laundering activities (Lokanan 2022;Chen et al. 2018;Turki et al. 2021;Canhoto 2021). The application of AI not only reduces the potential for error and oversight but also allows financial institutions to use fewer resources while achieving a higher level of accuracy in their AML obligations (Singh and Lin 2021;Garcia-Bedoya, Granados, and Cardozo Burgos 2021). ...
... ML and advanced AI-based technologies offer automated solutions for analyzing financial activities of customers and organizations and for detecting potential violations of AML regulations (Kurum 2020;Lokanan 2022;Rocha-Salazar, Segovia-Vargas, and Camacho-Miñano 2021). Recent research has found that AI-based tools can quickly and accurately assess historical transactions, customer types, and the history of each activity in large datasets and identify signals or trends that may lead to violation of AML sanctions (Kurum 2020;Singh and Lin 2021;Alkhalili, Qutqut, and Almasalha 2021;Turki et al. 2021). Research has also found that complex patterns can be understood in real time and over long periods, providing incredibly valuable insights for organizations seeking to comply with applicable regulations using AI-based solutions Nissan 2012;Ofoeda et al. 2022;Masciandaro and Filotto 2001;Ni and Wang 2022). ...
This study employs machine learning techniques to identify key drivers of suspicious activity reporting. The data for this study comes from all suspicious activities reported to the California government in 2018. In total, there were 45,000 records of data that represent various features. The paper uses linear regression along with Lasso, Ridge, and Elastic Net to perform feature regularization and address overfitting with the data. Other probabilistic and non-linear algorithms, namely, support vector machines, random forests, XGBoost, and CatBoost, were used to deal with the complexity of the data. The results from the mean squared and root mean squared errors indicate that the ensemble tree-based algorithm performed better than the statistical and probabilistic models. The findings revealed that filings from regulators, the type of products, and customers' relationships with the institutions were the top contributors to SAR filings. Through the evaluation of a vast amount of data, this study provides valuable insights for identifying suspicious activities in financial transactions and has the potential to significantly improve suspicious transaction monitoring.
... The term (Artificial Intelligence) was formed by the Professor John McCarthy in 1956 and he was awarded in 1971 for his extensive researches in this domain. There are four categories of Artificial Intelligence that consist of: reactive machines, limited memory, theory of mind and self-awareness (Turki et al., 2021). ...
... However, there are still many difficulties in applying them effectively in Palestine, mainly the electronic Signature, due to factors. The two researchers will shed light on the various RegTech and electronic laws related to electronic Signatures and the obstacles to their application in Palestine (Issa 2001;Turki et al., 2020Turki et al., , 2021Harraf et al., 2020). ...
... The term (Artificial Intelligence) was formed by the Professor John McCarthy in 1956 and he was awarded in 1971 for his extensive researches in this domain. There are four categories of Artificial Intelligence that consist of: reactive machines, limited memory, theory of mind and self-awareness (Turki et al., 2021). ...
... However, there are still many difficulties in applying them effectively in Palestine, mainly the electronic Signature, due to factors. The two researchers will shed light on the various RegTech and electronic laws related to electronic Signatures and the obstacles to their application in Palestine (Issa 2001;Turki et al., 2020Turki et al., , 2021Harraf et al., 2020). ...
The public sector organizations are increasingly interested in using data science and capabilities of artificial intelligence (AI) to engender administrative and support services. The AI is identified for deepening the scientific knowledge in extents of administration, support services and addressing public queries. Other than private sector, the public services are focusing towards the advancement of technology through the arrangement of AI. Correspondingly, this chapter strives to view the artificial intelligence within the domains of governance while intervening in public services and localities during COVID-19. It considers the factors that the study endorses with opportunities and challenges that disclose the prospects in the current scenario. The study aims to highlight the use of AI in governance and support services through the online survey from the local departments and administrative service providers. The scope addressed in the study included the key state divisions, trailed by the National Health and Services, Science and Technology, Relief and Welfare departments, along with the key stakeholders in localities. The study examines the facilitative governance, shared objectives, imparting information, communication, socializing, AI expertise, and decision making over coronavirus governance. The results reflect a comprehensive strategy through artificial intelligence application to minimize the devastation caused by the event of coronavirus. It also invites public and private parties to support the government in technological affairs of governing the crisis situation of COVID-19. The research contributed for further investigation in the tools that can better the state governance services towards common citizen of Pakistan. Significant future implications are noted in view of the challenges and opportunities received and incorporated in the study.
... The term (Artificial Intelligence) was formed by the Professor John McCarthy in 1956 and he was awarded in 1971 for his extensive researches in this domain. There are four categories of Artificial Intelligence that consist of: reactive machines, limited memory, theory of mind and self-awareness (Turki et al., 2021). ...
... However, there are still many difficulties in applying them effectively in Palestine, mainly the electronic Signature, due to factors. The two researchers will shed light on the various RegTech and electronic laws related to electronic Signatures and the obstacles to their application in Palestine (Issa 2001;Turki et al., 2020Turki et al., , 2021Harraf et al., 2020). ...
This book focuses on the implementation of Artificial Intelligence in Business, Education and Healthcare, It includes research articles and expository papers on the applications of Artificial Intelligence on Decision Making, Entrepreneurship, Social Media, Healthcare, Education, Public Sector, FinTech, and RegTech. It also discusses the role of Artificial Intelligence in the current COVID-19 pandemic, in the health sector, education, and others. It also discusses the impact of Artificial Intelligence on decision-making in vital sectors of the economy.
... It automates regulatory monitoring, reporting & compliance thereby enhancing the process of delivering regulatory requirements for financial institutions [174]- [176]. Regtech is the blend of regulation in combination with the technology, aimed at improving and enhancing the efficiency, transparency and standardization of organization in terms of regulatory compliance [177], [178]. Regtech startups gather information online or from the web and use data analytics tools to determine anomalies and predict shady areas or patterns [179]. ...
... It automates regulatory monitoring, reporting & compliance thereby enhancing the process of delivering regulatory requirements for financial institutions [174]- [176]. Regtech is the blend of regulation in combination with the technology, aimed at improving and enhancing the efficiency, transparency and standardization of organization in terms of regulatory compliance [177], [178]. Regtech startups gather information online or from the web and use data analytics tools to determine anomalies and predict shady areas or patterns [179]. ...
The present study provides a systematic review of the major topics related to Fintech and Islamic finance that are associated with Fintech and Islamic finance since their inception post financial crisis of 2008 and even before. Fintech is a phenomenon; a disruption and it provides an opportunity to the Islamic finance to show its full potential and provide a true sustainable, ethical, and alternative financial system to the world. The study identifies and systematically examines the relevant literature on innovative financial services engineered by Fintech, scope of the Fintech based innovations, challenges, and implications of Fintech for the Islamic finance industry. The study aims to (1) examine the state of art in the Fintech based innovations and its implications in the Islamic finance industry; (2) identify the gaps in the Islamic Fintech research and make suggestions for the further scope of the study; (3) provide a detailed overview of the Fintech based financial services and related unique issues and challenges related to its implications in Islamic finance industry. The novel proposition in this study includes enriching the Islamic fintech literature. The result of the study provides a theoretical foundation for future research in Islamic Fintech including the development of innovative financial services and its sharia compliance.
... In essence, these studies aim to detect money laundering transactions in a binary classification setting. While machine learning based automation is increasingly deemed as a valuable mean in AML for tasks such as transaction monitoring (Turki et al., 2020) and processing client information (Tiwari et al., 2020), a sensitive field such as AML cannot solely rely on machine learning algorithms to replace human expertise for reporting illegal transactions. Instead, compliance teams have an obligation of providing their reasoning when filing suspicious activity reports (Singh and Lin, 2020). ...
There has been an emerging interest by financial institutions to develop advanced systems that can help enhance their anti-money laundering (AML) programmes. In this study, we present a self-organising map (SOM) based approach to predict which bank accounts are possibly involved in money laundering cases, given their financial transaction histories. Our method takes advantage of the competitive and adaptive properties of SOM to represent the accounts in a lower-dimensional space. Subsequently, categorising the SOM and the accounts into money laundering risk levels and proposing investigative strategies enables us to measure the classification performance. Our results indicate that our framework is well capable of identifying suspicious accounts already investigated by our partner bank, using both proposed investigation strategies. We further validate our model by analysing the performance when modifying different parameters in our dataset.
Purpose
This study aims to explore several challenges in the use of regulatory technologies (RegTech) in Islamic and conventional financial markets and share recommendations in this regard.
Design/methodology/approach
A qualitative research methodology was used to identify the existing challenges. Literature was reviewed and analyzed, and seven experts were interviewed or consulted online and their feedback examined. The judgment of the case B2C2 Ltd v Quoine Pte Ltd. was reviewed.
Findings
This study reveals a lack of relevant regulatory frameworks capable of meeting some of the evolving challenges, lack of awareness among market players and lack of expertise in RegTech. The list of additional challenges includes the issue of legacy technology, the weaknesses of human programmers and the need for a multifaceted solution for compliance requirements.
Research limitations/implications
This study notes the novelty of RegTech in the financial world, especially in the Islamic financial market. Thus, there is a dearth of relevant literature. This study assists relevant conventional and Islamic financial market entities and authorities in determining the potential impact of RegTech on their respective businesses and the financial system.
Practical implications
This study proffers recommendations to assist in addressing the challenges facing its users and paving the way for innovative solutions that will facilitate and enhance the use of RegTech in financial markets. Regulators and other stakeholders of the financial industry will learn from the challenges identified and can review the recommendations for adoption. Apart from that, the decision of B2C2 Ltd v Quoine has practical implications for RegTech users, as the court in B2C2 Ltd v Quoine accessed the “knowledge” of the programmers of deterministic software at the time of the coding.
Originality/value
RegTech can offer cost-effective and efficient means to comply with regulations and ensure the accuracy of the information provided to regulators. This study provides a better understanding of the challenges associated with its use. The recommendations include enactment of a blueprint for a digitally enabled regulatory framework, creating awareness of RegTech via stakeholder roundtable discussions, development of human talent, formulating human governance standards and finding innovative ways to manage risks.
Financial Technology (Fintech) is the implementation of the latest technology advancement to enhance financial operations. It is used to automate these operations and make them more simple, improve their productivity and quality and reduce human interactions. Fintech payment is used widely to perform financial transactions electronically through many channels and products. Fintech is used to advance Payments by implementing technology in Credit and Debit Cards, constantly improving mobile and online payments and banking, attaching new features in ATMs, and allowing customers to initialize digital wallets. During the pandemic of Covid-19, there has been an increase in the usage of Fintech payments technologies. In addition, many payment services have been introduced to help with the procedures implemented to reduce the spread of the virus. This literature review concentrates on Fintech payments and focuses on factors affecting the usage of these payments. The factors which will be focused on are Covid-19, Regulations, Customer Experience, and Quality of payments products and services.KeywordsFintechPaymentCovid-19BankingArtificial intelligenceData scienceE-walletMobile paymentDebit cardCredit card
Considering the difficulties of the dispatching control system in smart distribution network，such as wide ac⁃
cess，lots of influencing factors and difficult quantitative evaluation，a set of core index is constructed for the dispatch⁃
ing control system in smart distribution network. Firstly，the task scheduling and running states of smart distribution net⁃
work are analyzed with the combination of situations both at home and abroad. Then，the core index system is construct⁃
ed，including 21secondary indexes from 4 aspects（i.e.，safety and reliability，high economic efficiency，advanced tech⁃
nology，and environmental friendliness）. Finally，the operation of smart distribution network in two typical regions is
analyzed. The index system built in this paper can not only reflect the running state of smart distribution network，but al⁃
so effectively guide the operation and maintenance personnel to enhance their professional ability.
Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to retrieve open street map (OSM) spatial data. This study also proposes finding hotpoints extracted from crime hotspots area found by Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A spatial distance feature is then computed based on the position of different hotpoints for various types of crime and this value is used as a feature for classifiers. We test the engineered features in crime data from Royal Canadian Mounted Police of Halifax, NS. We observed a significant performance improvement in crime prediction using the new generated spatial features.
Data clustering is a process of arranging data into groups or a technique for classifying a mountain of information into some manageable meaningful piles. The goal of clustering is to partitions a dataset into several groups such that the similarity within a group is better than among groups. K- means is one of the basic clustering algorithm which is commonly used in several applications, but it is computationally time consuming and the quality of the resulting clusters heavily depends on the selection of initial centroids.We can removed first limitation using the Enhanced K- Means algorithm. This paper represent the comparison analysis of basic K-Means clustering algorithm and Enhanced K- Means clustering algorithm which shows Enhanced K-Means algorithm more effective and efficient than Basic K-means algorithm.
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