A Novel Multivariate Classification Model using Triangular Boundary based Outlier Detection (TBOD) Algorithm to Discover Suspicious Transaction Activity

The fight against money laundering (ML) remains a significant challenge for global financial systems. Traditional anti-money laundering (AML) measures often struggle with inefficiencies, high compliance costs, data fragmentation, and the increasing complexity of illicit financial activities. Blockchain technology, with its unique features of decentralization, transparency, and immutability, presents a next-generation solution to transform AML compliance and disrupt money laundering networks. By providing real-time transaction monitoring, transparent audit trails, and secure data sharing, blockchain enhances the ability to trace illicit funds across borders and financial systems, offering a level of visibility and accuracy previously unattainable. This explores how blockchain innovations can address the core limitations of existing AML frameworks. Blockchain’s immutable ledger allows for the secure tracking and verification of financial transactions, making it nearly impossible to manipulate or tamper with data. Additionally, smart contracts enable automated compliance checks, such as reporting suspicious activities or freezing assets when certain conditions are met, streamlining the compliance process and reducing human error. Furthermore, blockchain’s decentralized nature facilitates cross-border collaboration by allowing for the secure sharing of AML data between jurisdictions, fostering global cooperation without compromising privacy or security. Blockchain’s potential also extends to enhancing Know Your Customer (KYC) protocols, enabling secure, decentralized identity management systems that reduce fraud risks and redundancy. Despite its promising applications, the widespread adoption of blockchain for AML compliance faces challenges related to scalability, integration with legacy financial systems, and regulatory harmonization. Nevertheless, blockchain represents a powerful tool for disrupting money laundering networks and enhancing the efficiency and transparency of AML efforts in an increasingly digital financial ecosystem. This argues that blockchain will play a critical role in the evolution of AML strategies, offering a more secure and efficient solution to combat financial crime.

Citation (2023), "Index", Tyagi, P., Grima, S., Sood, K., Balamurugan, B., Özen, E. and Eleftherios, T. (Ed.) Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy (Contemporary Studies in Economic and Financial Analysis, Vol. 110B), Emerald Publishing Limited, Bingley, pp. 265-276. https://doi.org/10.1108/S1569-37592023000110B018 Publisher: Emerald Publishing Limited Copyright © 2023 Pallavi Tyagi, Simon Grima, Kiran Sood, B. Balamurugan, Ercan Özen, and Thalassinos Eleftherios INDEX Accounting, 146 Actual economic activity, 175 Administrative security controls in SME, current state of, 35–37 Africa, effect of COVID-19 debt accumulation, 165 Agriculture, 133 Alternative finance models, 102 Amazon (technology company), 98 Annual appraisals, 252 Anti-black Money Act (2015), 126 Anti-money laundering (AML), 60, 172 Apple (technology company), 98 Artificial intelligence (AI), 23, 85–86, 172, 217, 244, 252–253 ability of AI and SA to improve PM systems, 257 application of AI and SA in business processes, 255 automating PM Systems, 259–261 in business processes, 255–256 classification models to predict employee performance, 257–259 classification of AI, based on capabilities, 253–254 classification of AI, based on functionality, 254 competition, 219 demographics of customers, 219 drivers of digitisation and AI in retail, 218 in HR management, 259 limitations of AI and SA in PM systems, 261–262 for mapping cybersecurity controls for SME, 46–48 in PM systems, 257 retail customers, 218–219 retail digitisation using, 219–220 super, 254 technology and, 218 types, 253 Aspirational Districts Programme, The, 88 Asset management segment, 99 Augmented reality, 23 Authors’ network analysis, citation as per, 68–69 Autonomous robotics, 23 Balance sheet lending (BSL), 100 Bank 4.00, 102 Bank of Baroda, 7, 11–12 Bank of Hindustan, 2 Banking, 2, 98, 206 co-operative banks, 2 commercial banks, 2 industry, 205 payments banks, 2 small finance banks, 2 Banking, financial services, and insurance (BFSI), 29 Banking Regulation Act (1949), 2 Banks, 205 Bibexcel, 135 Bibliometrics, 57, 60 analysis, 141 of entrepreneurial universities, 57 R Package, 135 research, 141 Bibliometrix, 178 Biblioshiny, 178 Big data, 23, 85, 255 analytics, 84 BigTechs, 101 Billion Prices Project, 87 Black box model, 85 Black money, 58–59 Blending data sciences in economic and social issues, 86–87 Blockchain, 132 content analysis, 137–141 data analysis, 135–137 directions for future research, 141–142 implications, 141 methodology, 133–134 technology, 132 Bombay Stock Exchange (BSE), 75–76 Bootstrapping methods, 227 Bring your own device policy (BYOD policy), 40 Business domain-wise mission critical asset security (Business DMCAS), 43–44 Business to-Person lending models (B2P lending models), 101 Business-to-Business lending models (B2B lending models), 101 Businesses, 230 AI in, 255–256 changing priorities of CIA triad based on business domain, 30–31 SA in, 256–257 Buy Now Pay Later (BNPL), 101 C4.5 decision tree, 258 Canara Bank, 7, 9–10, 18 Capability maturity model (CMM), 26 Cashkumar (P2P lending platform), 107 version 2. 0, 108 Cashkumar Peer-to-Peer Lending Platform (Cashkumar P2P Lending Platform), 107–108 digital lending, 100–107 emotional analysis, 116–117 FinTech, 98–100, 108–109 literature review, 108 negative reviews, 119–120 P2P lending, 109–111 positive reviews, 118–119 research methodology, 112 sentiment analysis perspective, 111–112 theoretical background, 107 Castle approach, 28 Casual conversations, 250 Central Bank, 193 Central bank digital currency (CBDC), 190 Central Bank of Nigeria, 190 CIA triad assess priority of CIA triad for DMCA, 43 changing priorities of CIA triad based on business domain, 30–31 CIA trinity, 27 Citation network analysis, 67–68 citation as per authors’ network analysis, 68–69 Cloud computing, 23, 86 Co-authorship analysis, 60 Co-authorship network analysis as authors on money laundering, 62–66 as countries, 64–66 Co-occurrences analysis, 60 of keywords analysis on money laundering, 66–69 network analysis on money laundering, 66 Co-operative banks, 2, 4 Commercial banking, 2 Commercial banks, 2 foreign banks, 3 private sector banks, 3 public sector banks, 3 RRBs, 3 Communications service providers, 101 Companies Act, 105 Competitive pressure, 229 Conceptual model constructs and items measured, 221–223 development of, 221 Conceptual research model, 221 Confidence interval (CI), 227 Consumers, 216 Consumption of goods and services, 22 Content analysis, 137, 179 digitisation of agriculture, 140 supply chain management, 140 sustainable economic development, 140–141 Cooperative savings, 59 Cooperative Societies Act (1912), 4 Core cybersecurity concepts, 26–31 changing priorities of CIA triad based on business domain, 30–31 CIA Triad, 27 Defence in Depth, 28–29 DMCA, 29–30 Coronavirus, 162 Corporation threat agents, 24 Corruption, 56, 124 Cost-benefit analysis, 84 COVID 19, 5, 218 Africa, 165 effect of COVID-19 debt accumulation, 164 debt crisis, 164 debt statistics, 162–164 Europe, 164–165 global debt crisis, 161 heavily indebted countries, 167–168 and Indian Banking, 5 literature review, 161–162 methodology, 161 multilateral organisations, 166–167 pandemic, 160, 234–235 rich countries, 165–166 solution to overcome, 165 Cross industry standard process for data mining (CRISP-DM), 257 Crowdfunding, 99–100 Cryptocurrencies, 98 Customer relationship management (CRM), 253 Customers, 229 demographics of, 219 screening process, 58 Cutting-edge technology, 58 Cyber assets, 27 Cyber risk, 23 Cyberattacks faced by SMEs, experience of, 39–40 Cybercrime, 56 Cybercriminals, 37, 40 Cybersecurity, 23 calculate SME’s minimum overall cybersecurity controls implementation level, 44–45 implement prioritised cybersecurity controls for Business DMCAS, 43–44 responsible AI for mapping cybersecurity controls for SME, 46–48 Cybersecurity framework (CSF), 25 Cyberspace, 23 Data analysis, 224 measurement model, 224–226 structural model, 226–229 test, 224 tool, 88 Data collection, 223–224 Data extraction, 134 Data lags, 91–92 Data localisation problem, 92 Data mining, 85 techniques, 90 Data privacy and security, 91 Data science, 86 techniques, 85 Data-driven policy, India’s stance on, 87–89 Debt crisis, 161, 164 Debt restructuring, 162 Debt Service Suspension Initiative (DSSI), 166 Debt statistics, 162 external debt stocks, 162–163 massive spending by richer countries, 163–164 sovereign default, 164 Decision Tree, 257 Decision-making management, 173 Defence in depth (DiD), 28 Delay payments, 204 Democratisation of opinions, 236 Descriptive analysis, 60 Desktops, 40 Digital circuits, 22 Digital government, 87 Digital India campaigns, 89 Digital lending, 100 alternative finance models, 102 ecosystem, 100–102 functional business models in India, 105–107 funds transfer mechanism as per RBI guidelines, 105 global scenario, 101 governing enactments, 104–105 India, 102 landscape, 102–104 market share, 102 Digital sentiments, 236 Digital technologies, 109 Digital transformation, 216 in agriculture, 141 Digitalisation process of banking industry, 6 Digitisation intelligence competition, 219 demographics of customers, 219 drivers of digitisation and AI in retail, 218 retail customers, 218–219 technology and AI, 218 Digitisation of agriculture, 133, 140 Digitisation transforms retailing exchanges, 220 Directorate of Enforcement (ED), 126 Dirty money, 124 Document-term matrix (DTM), 178 Domain-wise Least Cybersecurity Implementation Framework (DLCI Framework), 41 assess priority of CIA triad for DMCA, 43 calculate SME’s DLCI Level, 45–46 calculate SME’s DMCAS Level, 44 calculate SME’s minimum overall cybersecurity controls implementation level, 44–45 identify DMCA, 41–43 implement prioritised cybersecurity controls for business DMCAS, 43–44 responsible AI for mapping cybersecurity controls for SME, 46–48 WCCI for SME, 44 Domain-wise Mission Critical Asset (DMCA), 29–30 Domestic lending by banks, 205 Drug trafficking, 56 e-CNY, 192 E-commerce platforms, 101 sites, 216 Earnings management, 146 conceptual framework, 147 income-targeting approach, 150–151 literature review, 148–150 priority theory of sustainable finance, 147–148 surplus income model, 152–155 surplus income model for sustainability, 151–152 Econometric models, 84 Economic and financial performance in topic modelling framework, relationship between money laundering and, 184–185 Economic growth (EG), 125, 128, 161, 173, 201 Electric vehicles infrastructure, investment target for, 202 Electronic word of mouth (e-WOM), 235 Embed quality assurance, 47 Emotion Lexicon (EmoLEX), 111 Emotional analysis, 116–117 Emotions, 116 Employees, 245, 258 in India, 175 narrow assessment of, 252 performance, 245 threat agents, 24 eNaira, 190, 193–195 CBDC, 190 money, 190 performs, 190 speed wallet, 196 Environmental, social and governance (ESG), 76, 79–81 ESG-based socially responsible investments, 75 Indian investors looking for, 75–76 Environmental accounting, 149 Environmental reporting, 146 Europe, effect of COVID-19 debt accumulation, 164–165 European Central Bank (ECB), 162 External debt stocks, 162–163 External incentives, 154 External stakeholders, 248 Facebook (technology company), 98 Federal Bank, 7, 13–14, 18 Finance models, 102 Financial Action Task Force (FATF), 58, 125 Financial crimes action plan for resolving financial crimes using SRI–ESG model, 81 channel for, 77–78 effects of, 79 terrorism financing devastating part of, 78–79 impact of unethical investments on, 79 Financial crisis (2008), 98 Financial industry, 205 Financial institutions, 205 in achieving sustainable economic goals, 206–207 through priority sector lending, 208–210 Financial markets, 101, 104 Financial performance Bank of Baroda, 11–12 Canara Bank, 9–10 co-operative banks, 4 COVID 19 and Indian Banking, 5 COVID 19, 5 data analysis, 8 data collection, 7 Federal Bank, 13–14 findings, 17–18 foreign banks, 3 HDFC Bank, 14–15 hypothesis testing for net profit, 16–17 hypothesis testing for NPA, 17 ICICI Bank, 12–13 Indian Bank, 10–11 limitation of study, 8 literature review, 5–7 non-performing assets, 4 non-scheduled banks, 4 payments banks, 4 private sector banks, 3 public sector banks, 3 research methodology, 7 research methodology, 8 research objectives, 7 RRB, 3 sample frame, 7 schedule banks, 4 small finance banks, 3–4 in topic modelling framework, relationship between money laundering and economic and, 184–185 Union Bank, 10 Yes Bank, 15–16 Financial services, 98 Financial study, 58 Financial Technology (FinTech), 98 branches of FinTech Industry, 99–100 classified into the following types, 98–99 credit, 102 landscape, 99 lenders, 101 start-ups, 109 Financing segment, 99 Firms, 147 Five-year planning process, 87 Forecasting methods, 84 Foreign Banks, 3 Formal process in PM systems, 249–250 Fugitive Economic Offenders Act (2018), 126 Functional business models in India, 105–107 Funds transfer mechanism as per RBI guidelines, 105 Global economy, 160 Global reporting initiative (GRI), 149 Globalisation of economies, 124 Gold medal, 238 Goods and Service Tax, 126 Google (Private companies), 91, 98 reviews, 114 Governance in India, 87 reporting, 146 Grameen Foundation, The, 87 Green accounting, 149 Green banking assessing funding needed by international agency, 205 investment target for electric vehicles infrastructure, 202 investment target for renewable energy generation, 201–202 investment target to set up smart cities, 203–204 methodology, 206 results, 206 role of financial institutions in achieving sustainable economic goals, 206–207 role of financial institutions through priority sector lending, 208–210 Green development, 210 Green energy, 210 Green growth, 206 Gross domestic product (GDP), 160, 174, 205 HDFC Bank, 7, 14–15, 18 Human bias, 252 Human resources (HR), 172, 255 AI in HR management, 259 Human-based PM systems, 252 Hume’s Aesthetic Theory, 108 Hypothesis testing for net profit, 16–17 for NPA, 17 ICICI Bank, 7, 12–13, 18 ID3 decision tree, 258 Illicit drug trafficking, 59 IMF Fact Sheet, 125 Income smoothing, 151 Income-targeting approach, 150–151 Incremental approach, 84 India current market trend of, 74–75 digital lending, 102 functional business models in, 105–107 measurements to control money laundering terror funding in, 125–126 retail scenario in, 217–218 SRI in, 75 stance on data-driven policy, 87–89 Indian Bank, 7, 10–11 Indian banking industry, 4 sector, 2 Indian Economy future implication of study, 128 impacts of money laundering on, 126–127 impacts of terror financing on, 127–128 measurements to control money laundering terror funding in India, 125–126 nexus of money laundering and terrorism financing, 125 Indian government, 90 Indian investors looking for ESG Investments, 75–76 Industry 1.0, 22 Industry 2.0, 22 Industry 4.0 digitisation, 23 Inferential research approach, 223 Informal economy, 174 Informal process in PM systems, 250 Information and communications technologies (ICTs), 23, 88, 217 assets, 27 Information security, 27 Information security management system (ISMS), 24 Information technology, 124 Institute for Energy Economics and Financial Analysis (IEEFA), 201 Insurance, 98 Intangible assets, 27 Integration of economies, 124 Interest-bearing eNaira, 194–195 Internal stakeholders, 248 International agency, assessing funding needed by, 205 International Energy Agency (IEA), 205 International Monetary Fund (IMF), 163, 175 International Standards Organization, 260 Internet of Things (IoT), 23, 84, 86, 255 Investment, 98 management, 175 target for electric vehicles infrastructure, 202 target for renewable energy generation, 201–202 target to set up smart cities, 203–204 IP Act (2000), 91 Keynesian model, 193 Know Your Customer (KYC), 106 Knowledge management cybersecurity, 40 Laptops, 40 Latent Dirichlet allocation (LDA), 179 Lender’s Club, 110 Lending policies, 205 Lending practices, 206 Link strength, 137 Logical controls, 35 Machine learning (ML), 86, 257 Malware assaults, 39 Management accounting, 148 Managerial discretion, 147 Managers and employees, 245–246 Market share, 102 Marketers, 218 Marketplace lending (MPL), 100 Massive data analysis approach, 59 Measurement model of data analysis, 224–226 Medal, 238 Mendeley database, 133 Mentor–Protege relationship, 250 Micro, small and medium enterprises (MSMEs), 208 Microsoft (Private companies), 91 Minerals, 30 Mission critical assets, 29 MIT Sloan School of Management, 87 Modern banking, 2 Money, 56 Money laundering, 60, 79, 124, 172–173 analysis of word cloud, 181 annual trend of publication, 61 citation network analysis, 67–69 co-authorship network analysis, 62 co-authorship network analysis as authors on, 62 co-authorship network analysis as countries, 64–66 co-occurrences network analysis on, 66–67 countries analysis, 62 data and methodology, 175–179 descriptive analysis, 179–180 historical background of, 58–60 impacts of money laundering on Indian economy, 126–127 investigating TF-IDF, 182–184 link between words, 181 literature review, 173–175 measurements to control money laundering terror funding in India, 125–126 nexus of, 125 relationship between money laundering and economic and financial performance in topic modelling framework, 184–185 research methodology and data, 60 results, 61 sources analysis, 61–62 Money lending, old concept of, 100 Mordor Intelligence, 89 Naïve Bayes classifiers, 258 Narcotic Drugs and Psychotropic Substances Act (NDPS), 59 NASSCOM, 89 National Association of Securities Dealer Automated Quotations, The, 98 National Strategy for Artificial Intelligence by NITI Aayog, 88 Natural catastrophes, 24, 34 Natural disasters, 34 Neo banks, 101 Net profit, hypothesis testing for, 16–17 Netnograhphy, 236 Nigeria CBDC, 190 current features of, 193–194 interest-bearing eNaira, 194–195 literature review, 191–193 no transaction costs, 195 redesigning, 194 security, 195–196 NIRAMAI, 88 NIST, 25 NITI Aayog, 88 Non-banking financial corporations (NBFCs), 100, 206 Non-performing Assets, 4 Non-performing loans (NPL), 162 Non-scheduled banks, 2, 4 Normalisation, 178 NPA, hypothesis testing for, 17 Offline P2P lending, 100 Olympic Games Tokyo (2020), 237 Olympics, 240 destination, 234 players, 239 Online P2P lending platforms, 100, 109–110 Online platforms, 40 Opaque system, 252 Open Government Data (OGD), 87 Opensource Stack software, 87 Oracle (Private companies), 91 Organisation-owned devices, 40 Organisational goals, 246 Organisations, 244 Organised retailers, 230 Organised retailing, 221, 223 Palantir Technologies (American software company), 87 Pandemic, 160 Parallel economy, 124 Partial least square (PLS), 224 Payment, 191 Payments Banks, 2, 4 Performance appraisal (PA), 244 Performance assessments, 249 Performance improvement, notifications of, 249–250 Performance management systems, 244–245 AI and SA, 252–257 AI and SA in PM systems, 257–262 components of, 245–246 formal processes, 249–250 informal processes, 250 inputs to PM systems, 250–251 issues in current PM systems, 251–252 monitoring, 246–247 outputs from PM systems, 251 planning, 246 principles of PM systems, 248–249 processes in PM systems, 249 reviewing, 247 rewarding, 247 stakeholders in PM systems, 247–248 tasks in PM Systems, 246 Person-to-Business lending models (P2B lending models), 101 Person-to-Person lending models (P2P lending models), 101, 109–111 crediting, 109 elements, 100 lending platforms, 100–101, 106 lending services, 104 Physical retailers, 216 Physical security current state of physical security controls in SME, 34 measures, 34 Physical stores, 217 Planning–programming–budgeting system, 84 Platform lending, 100 Policy-making process, 84 in India, 87 Predictive modelling techniques, 85 Prevention of Money Laundering Act (PMLA), 59, 126 Priority sector lending role of financial institutions through, 208–210 Priority theory of sustainable finance, 147–148 PRISMA 2020 Flow Diagram, 133 Private Banks, 8, 18 Private companies, 91 Private giants, 89 Private Sector Banks, 3 Production-linked Incentive Scheme (PLI Scheme), 207 Profitability, 5 of Indian banks, 7 Programmable integrated circuits (PLCs), 22 Public policies, 84 Public Sector Banks, 3, 8, 18 Python application, 112, 114 Qualitative research techniques, 85 Quality assessment, 134 Quasi-legal enterprises, 56 Real-time resource monitoring techniques, 85 Refugee Olympic, 237 Regional Rural Banks (RRB), 2–3 Relative frequency, 183 Renewable energy, 22, 201, 207 investment target for renewable energy generation, 201–202 Research methodology, 85 Reserve Bank of India (RBI), 2, 6, 99, 207 funds transfer mechanism as per RBI guidelines, 105 Reserve Bank of India Act (1934), 2, 4 Resource allocation, 76 Retail business analytics, 219 Retail customers, 218–219 Retail digital promotion, 220–221 Retail industry, 221 Retail scenario in India, 217–218 Retail stores, 216 Retailers, 216–217, 229 Retailing, 220 data analysis, 224–229 data collection, 223–224 development of conceptual model, 221–223 drivers of digitisation and AI in retail, 218–219 implications of study, 229–230 limitation, scope for further research, 230 literature review and hypothesis development, 217 research methodology, 223–223 research objectives, 217 retail digital promotion, 220–221 retail digitisation using AI, 219–220 retail scenario in India, 217–218 Reward system, 247 Rio Olympic (2016), 236 Scalability, 252 Schedule Banks, 4 Scheduled banks, 2 Search strategy, 133 Security awareness training for employees in SMEs, frequency of, 37–38 Security controls in SME, current state of, 34 Security objectives, 27, 29 Selection criterion, 133–134 Sentiment analysis, 238 method, 114 perspective, 111–112 Sentiments, 236 Sin stocks, 80 Small and Medium Enterprises (SME), 22, 31 age of, 32 analysis of research interview results, 41 analysis of research survey results, 32 biggest problems faced by SMEs implementing or deciding/planning to implement cybersecurity controls, 38–39 core cybersecurity concepts, 26–31 current state of administrative security controls in, 35–37 current state of implemented standards or framework in, 32–33 current state of physical security controls in, 34–35 current state of security controls in, 34 current state of technical security controls in, 35 DLCI framework, 41–48 DLCI Level, 45–46 DMCAS Level, 44 experience of cyberattacks faced by, 39–40 frequency of security awareness training for employees in, 37–38 literature review, 24–26 methodology, 31–32 minimum overall cybersecurity controls implementation level, calculate, 44–45 responsible AI for mapping cybersecurity controls for, 46–48 WCCI for, 44 Small and medium-sized businesses (SMBs), 23, 101 Small business owners, 109 Small finance banks, 2–4 Smart Analytics, 252, 254–255 ability of AI and SA to improve PM systems, 257 AI in HR management, 259 application of AI and SA in business processes, 255 automating PM Systems, 259–261 benefits of, 255 classification models to predict employee performance, 257–259 limitations of AI and SA in PM systems, 261–262 in PM systems, 257 SA in business processes, 256–257 Smart cities, investment target to set up, 203–204 Social accounting, 148 Social media, 219, 235 Social reporting, 146 Socially responsible investments (SRI), 74–76, 79–81 action plan for resolving financial crimes using SRI–ESG Model, 81 current market trend of SRI in India, 74–75 effects of financial crimes, 79 ESG and SRI, 79–81 findings of study, 77 heterogeneity, 76 in India, 75 investors, limitations of study, 77 research 77 research methodology, 77 review of socially responsible 75–76 76 terrorism financing devastating part of financial 78–79 impact of unethical investments on financial crimes, 79 unethical investors, 77–78 41 energy generation, 201 234 234 239 239 149 in PM Systems, 247–248 107 Bank of India 2 Data Analysis, 224 theory 235 modelling 224 model of data analysis, 226–229 178 chain management, 140 income model, implication of model, implications for or for sustainability, 151–152 profit, 206 149 Sustainable development, 206 Sustainable development Sustainable economic development, 140–141 Sustainable economic goals, role of financial institutions in 206–207 Sustainable 147 Sustainable growth in India, 206 security 26 18 249 (Private 89 98 controls, 35 security controls in SME, current state of, 35 132 and AI, 221 companies, 98 Technology and and model Technology governance data sciences in economic and social issues, 86–87 India’s stance on data-driven policy, 87–89 research and methodology, findings and 22 frequency 179 investigating TF-IDF, 182–184 financing on Indian economy, impacts of, 127–128 59 financing, 79 devastating part of financial 78–79 nexus of, 125 threat agents, 24 analysis, 178 data methods, 178 agents, 24 Tokyo Olympic (2020), 234 data analysis and findings, literature review, research methodology, 236 Tokyo Olympic 234 179 relationship between money laundering and economic and financial performance in, 184–185 profit, action 236 PM systems, 252 149 253 234 content analysis, 236 data, 236 sentiments, 235 laundering system, 59 investments on financial crimes, impact of, 79 investors, 77–78 payments 6 Union Bank, 7, 10 56 National Security 28 224 analysis, 60 60, 135 23 87 40 of 173 Cybersecurity Implementation 44 22 23 Yes Bank, 7, 18 25 COVID 19 and Financial Performance of Banks in and of Least Cybersecurity for Small and Medium Enterprises for Global Digitalised Economy Money of from to Socially for Financial Technology and of Governance and Policy-making for the the Sentiment Analysis of Cashkumar Peer-to-Peer Lending on Google of Money Laundering and Financing on the Indian Economy the on Implementation of in Earnings Management for Income of Sustainable COVID-19 Global Debt to the Analysis of Money Laundering Research the Central Bank Digital for Payments and Green Banking to Sustainable Economic Digitisation and Artificial Intelligence in Sector on Sentiment Analysis in 2020 Smart and AI for Modern Performance Management

The integration of Artificial Intelligence (AI) into Customer Relationship Management (CRM) has revolutionized the financial services industry by enhancing customer engagement, fraud detection, predictive analytics, regulatory compliance, and marketing strategies. This study systematically reviews 83 scholarly studies, including peer-reviewed journal articles, industry reports, and financial institution case studies, to assess AI’s impact on financial CRM. The findings indicate that AI-powered chatbots, virtual assistants, and sentiment analysis tools have significantly improved customer interactions, reducing response times by 57% and operational costs by 38%, while increasing customer retention rates by 28%. AI-driven fraud detection systems have enhanced transaction monitoring, reducing false positives by 52% and improving fraud detection efficiency by 74%, leading to a 43% decrease in financial losses related to fraud. Predictive analytics has transformed credit risk assessment, improving loan approval accuracy by 67%, expediting loan processing by 29%, and reducing default rates by 23%. AI has also optimized regulatory compliance by automating Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, increasing compliance accuracy by 58% and reducing penalties by 37%. Additionally, AI-driven marketing strategies have strengthened customer targeting, increasing engagement by 53% and boosting product adoption rates by 31%, while Customer Lifetime Value (CLV) models have contributed to a 27% increase in long-term customer retention and a 22% improvement in per-customer profitability. This study provides a comprehensive analysis of AI-driven CRM’s measurable benefits in financial services, demonstrating its role in enhancing decision-making, streamlining operations, improving financial security, and fostering long-term customer loyalty. The findings contribute to the expanding literature on AI in financial CRM and offer strategic insights for financial institutions, policymakers, and technology developers aiming to optimize AI adoption for sustainable growth and competitive advantage.

The rapid digitalisation of the Indian banking sector has revolutionised financial services, improving efficiency, accessibility, and customer experience. However, this transformation has also heightened the vulnerability of banks to sophisticated fraud, cybercrime, and financial misconduct. Conventional fraud detection techniques, often rule-based, struggle to keep pace with the evolving threat landscape. Consequently, artificial intelligence (AI)-powered fraud detection systems have emerged as a strategic solution to protect financial institutions and their customers. This paper explores the application of AI in fraud detection and risk management within the Indian banking system, analysing its impact on financial security, operational efficiency, and customer trust. It highlights how machine learning, natural language processing, predictive analytics, and behavioural biometrics are being deployed to detect anomalies, identify suspicious patterns, and prevent fraud in real time. Additionally, the study examines how AI enhances compliance with regulatory frameworks, particularly in areas such as anti-money laundering (AML) and Know Your Customer (KYC) obligations. Using secondary data sources, including research articles, industry reports, and case studies, this paper evaluates both the benefits and challenges of integrating AI into fraud detection frameworks. Findings suggest that while AI significantly reduces false positives, enhances scalability, and strengthens proactive fraud management, issues such as data privacy, model transparency, and infrastructural readiness remain pressing concerns. The research concludes that AI-powered fraud detection is not merely an optional innovation but a necessity for Indian banks to safeguard financial systems in an increasingly digital economy. Practical recommendations include fostering collaboration between banks and fintech firms, investing in AI research and training, adopting explainable AI models for regulatory trust, and strengthening cybersecurity awareness among customers. By leveraging AI responsibly and effectively, Indian banking institutions can build resilient systems that balance security with innovation, thereby enhancing long-term sustainability and public trust.

Regulatory technology (RegTech) is transforming financial compliance by integrating advanced information technologies to strengthen anti-money laundering and countering the financing of terrorism (AML-CFT) frameworks. Recent literature suggests that such technologies represent more than just an efficiency tool; they mark a paradigm shift in regulation and the evolution of financial oversight (Kurum, 2023). This paper aims to provide a narrative review of recent RegTech applications in financial crime prevention, with a focus on key compliance domains. A structured literature review was conducted to examine publications between 2020 and 2024 with a thematic synthesis of findings related to customer due diligence (CDD) and know your customer (KYC), transaction monitoring, regulatory reporting and compliance automation, information sharing and cross-border cooperation, as well as cost efficiency. Findings reveal that RegTech solutions give financial institutions more responsibility for detecting and managing financial crime risks, making them more active players in compliance processes traditionally overseen by regulators. The combined use of technologies such as artificial intelligence (AI), blockchain, and big data also generates synergistic effects that improve compliance outcomes beyond what these technologies achieve individually. This demonstrates the strategic relevance of integrated RegTech approaches.

Outlier detection shows its increasingly high practical value in many application areas such as intrusion detection, fraud detection, discovery of criminal activities in electronic commerce and so on. Many techniques have been developed for outlier detection, including distribution-based outlier detection algorithm, depth-based outlier detection algorithm, distance-based outlier detection algorithm, density-based outlier detection algorithm and clustering-based outlier detection. Spectral clustering receives much attention as a competitive clustering algorithms emerging in recent years. However, it is not very well scalable to modern large datasets. To partially circumvent this drawback, in this paper, we propose a new outlier detection method inspired by spectral clustering. Our algorithm combines the concept of kNN and spectral clustering techniques to obtain the abnormal data as outliers by using the information of eigenvalues and eigenvectors statistically in the feature space. We compare the performance of our methods with distance-based outlier detection methods and density-based outlier detection methods. Experimental results show the effectiveness of our algorithm for identifying outliers.

The United States — specifically through the US Department of the Treasury's Office of Foreign Assets Control (OFAC) and the Department of State — has maintained economic sanctions against certain Latin American countries since the 1950s. Starting with broad-based sanctions against Cuba, where OFAC prohibited virtually all transactions, the Department of the Treasury has instituted more-targeted sanctions regimes against Venezuela and Nicaragua, where non-government dealings are, for the most part, authorised. Together, the Departments of State and the Treasury also administer sanctions against various narcotics and terrorist organisations operating in Latin America. Most notable is the Foreign Narcotics Kingpin Designation Act, which has targeted trafficking organisations, foreign officials and their money laundering empires. To that end, sanctions, while initially imposed to fight certain political regimes, have developed into sanctions targeting all kinds of wrongdoing such as sponsoring terrorism, aiding and abetting drug trafficking, corruption and the commission of human rights violations. US sanctions violations are assessed under a strict liability standard (ie, no knowledge of the violation is required to trigger liability) and impact not only those directly subject to the jurisdiction of the US but also may carry implications for non-US persons undertaking certain ‘significant transactions' with sanctioned persons and jurisdictions. Further, while sanctions apply to US persons, OFAC can institute penalties on non-US persons for causing a US person to violate their compliance obligations. In addition, OFAC (or the Department of State) may sanction persons (individuals or entities) by adding them to the Specially Designated and Blocked Nationals (SDN) List. US persons must block the property and interests in property of SDNs, and are prohibited from virtually all, direct and indirect, transactions involving an SDN. SDN restrictions also apply to ‘Shadow SDNs' which are entities owned 50 per cent or more, directly or indirectly, by one or more SDNs in the aggregate. A Shadow SDN receives the same treatment as SDNs, regardless of whether such entity is itself on the SDN List. The challenge with Shadow SDNs is that they can only be discovered through rigorous due diligence into beneficial ownership structures of sometimes opaque corporate entities. This is where the world of sanctions compliance and enforcement crosses with that of anti-money laundering. The evolving customer due diligence, ongoing customer monitoring, suspicious activity investigations and reporting, and know your customer (KYC) (and know your customer's customer [KYCC]) evidenced are reflected through the evolving Financial Action Task Force (FATF) Recommendations, the Financial Action Task Force on Money Laundering in South America (GAFISUD) and domestic regulations. As a result of the increasingly unpredictable political changes in Latin America, and the concurrent changes in sanctions regimes, financial institutions (FIs) should maintain a proper understanding of the current sanctions prohibitions, the trends and updates in the area, but equally important, they should also maintain a good understanding of the complex legal provisions authorising certain behaviours — including the general licences and exemptions — that may apply. FIs must balance mitigating the risks of sanctions violations, and the associated hefty penalties, with the risks of a cumbersome and overly restrictive compliance regimen. An appropriately risk-based compliance programme that achieves both of these objectives is very much an art and not a ‘one size fits all’ model.

Financial services through the internet are running under various threats like phishing, pharming (cyber attack intended to redirect a website's traffic to another fake site), malware, and evolving sophistication of compromise techniques. Multi-factor authentication (MFA) financial service system alleviates the risk and makes it secure. Various methods of MFA run in troubles like the authentication device lost or stolen, false sense of security (if used on login device), compromised answer of the generic question, higher implementation costs, unambiguous movement profile, calculated hash value stolen (there is no chance to replace it), etc. Compliance with Anti-Money Laundering (AML), Know Your Customer (KYC) and sanction requirements continue to be a key focus area for Financial Institutions' (FIs) management; firms must ensure that they are following appropriate compliance procedures to meet the increasing regulatory demands [1,2]. By addressing existing limitation of MFA, this paper proposes dynamic KYC based MFA authentication method to ensure the secured access of the financial services through the internet. Analysis and simulation results show that the proposed method provides control same as existing MFA/2FA techniques, but customers will get relief from holding additional security devices that incur huge cost.

The detection of money laundering is an essential aspect in the modern fight against organized crime and financial corruption. Money laundering is a procedure used by criminals to disguise the legal source of ill-gotten gains and integrate them into the legal economy. In this essay, we will explore the meaning and methodology of money laundering detection. The initial purpose of the money laundering detection methodology is to identify and track suspicious financial transactions and activities of individuals and legal entities. Various techniques and tools are used for this, including financial monitoring of transactions, analysis of financial documents and investigation of ownership and assets. One of the most important aspects of the methodology for detecting money laundering is the analysis of financial transactions. Criminal investigators investigate all transactions of suspicious persons and organizations, to identify transactions that occur in the context of money laundering. Investigating ownership and assets is another critical part of the money laundering detection methodology. This includes checks on the properties and assets of suspicious individuals and organizations. Investigators look into property, patents, company ownership and other assets to prove the links between them and the origin of the money. In conclusion, the methodology for detecting money laundering is complex and very important in the fight against organized crime and financial corruption. Using the analysis of financial transactions, financial reports and documents, as well as the investigation of ownership and assets, criminals try to identify and prosecute money launderers and organizations. Using modern technology and international cooperation, the effectiveness of the methodology can be improved and more serious obstacles can be created for criminals trying to use it to hide illegal capital.

International audience

Objective: to research a new financial tool, namely, digital currencies of central banks, to analyze the essence of stablecoins as a new financial tool, and to assess the risks of non-compliance with the AML (“anti-money laundering”) and KYC (“know your customer”) requirements when stablecoins are distributed.Methods: the research used methods of system analysis, dialectical method and its derivatives, methods and principles of determinism, induction, deduction and hypothesis. Functional and statistical methods were used to systematize the obtained data.Results: digital currencies, though rejected by many states, are becoming more and more widely spread. In this regard, the concept of creating state-owned digital currencies is the most discussed one in the global financial community. In some countries, central banks are already testing them. However, their global spread has been impeded by concerns about the possibility of their use for criminal purposes. In the Russian Federation, where the state has currently no established position on the nature of digital financial assets, discussions are also underway on the possibility of creating an interstate digital currency. The article discusses the features and types of stablecoins and their advantages relative to other types of cryptocurrencies. The main advantages of the Central Bank’s digital currency and its application in accounting are considered. According to the study results, the main risks associated with the spread of state digital currencies were clarified.Scientific novelty: for the first time in the Russian legal science, the study evaluates the concept of digital currencies of central banks from the position of key risks that prevent their widespread distribution and implementation. The main risks in the field of countering money laundering and compliance with the KYC principle, which assumes the final beneficiary identification, are shown.Practical significance: the research results presented in the paper can be used to improve the state policy on the legalization of digital financial assets, including state digital currencies.

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Commercial banks' performance is fundamental to the economy as providers of financial services. In offering this service, commercial banks are exposed to a range of risks that negatively affect financial position and ultimately influence profitability. Profitability is indicative of a bank's stability and potential for growth. Enhancing commercial banks' profitability contributes to shareholder return on investment. In June 2018, five banks were fined a total of Kshs. 392 million by the Central Bank of Kenya for breaching anti-money laundering regulations. Consequently, banks had to invest resources to improve their anti-money laundering measures. Consequently, raising operational and compliance overheads. This study sought to determine the effect of transaction monitoring on profitability of commercial banks in Kenya. The research employed an explanatory research design. The targeted population comprised all the thirty-nine regulated commercial banks as of December 31, 2021. The study period was eight (8) years (2014 to 2021). Respondents were chosen through purposive sampling. Primary data was gathered using structured questionnaires, while secondary data was derived from audited financial reports of commercial banks and the annual banking supervision report from the Central Bank of Kenya. Subsequently, the collected data underwent analysis employing descriptive statistics and regression analysis. The research results disclosed that transaction monitoring positively and significantly influenced commercial banks profitability. Consequently, bank managers should incorporate transaction monitoring into their operations to augment the overall efficacy to detect and report potentially suspicious activities, and to strengthen operational controls.

Bank mergers have become increasingly common as financial institutions seek economies of scale, improved operational efficiency, and expanded market presence. However, mergers present significant regulatory compliance challenges due to differences in systems, processes, and regulatory reporting frameworks. Ensuring compliance with financial regulations such as anti-money laundering (AML), know-your-customer (KYC), and Basel III requirements becomes critical during post-merger integration. This study examines the role of information technology (IT) solutions in ensuring regulatory compliance during bank mergers. Using a case study approach of a mid-sized banking merger scenario, the research analyzes how integrated IT platforms—including core banking systems, regulatory reporting tools, AML monitoring systems, and data governance frameworks—support regulatory compliance throughout the merger process. The findings indicate that strategic deployment of IT solutions significantly reduces compliance risks, enhances transparency, and improves regulatory reporting accuracy. **Keywords—**Bank mergers, regulatory compliance, banking IT systems, AML, KYC, core banking integration, financial regulation.

Money laundering remains a major challenge to the operational efficiency and stability of commercial banks, particularly in emerging economies such as Nigeria, where financial transactions are increasingly complex and high-risk. This study examines the impact of money laundering activities on the operational performance of listed commercial banks in Nigeria, focusing on Suspicious Transaction Exposure, Effectiveness of Anti–Money Laundering (AML) Internal Controls, and Quality of Know-Your-Customer (KYC) Processes. A quantitative research design was adopted, with data collected through a structured questionnaire administered to compliance, risk management, internal audit, and operations professionals across listed commercial banks. Descriptive statistics, Pearson correlation analysis, and multiple regression techniques were used to analyse the relationships among the study variables. The findings show that suspicious transaction exposure has a significant negative effect on operational performance, while effective AML internal controls and high-quality KYC processes exert significant positive effects. AML internal controls emerged as the strongest predictor of operational performance. The results support Institutional Theory and Risk-Based Compliance Theory and highlight the need to integrate AML risk management into core banking operations to enhance efficiency and resilience. Keywords: Money Laundering Activities; Suspicious Transaction Exposure; AML Internal Controls; Know-Your-Customer Processes; Operational Performance of Banks.