Reinforcing CBDC Integrity: A Novel Anti Money Laundering Solution by Integrating Blockchain, Machine Learning, and Taint Analysis

Asset-backed securities (ABS), 147 Asset-backed tokenization, 153 Asset-backed tokens (ABTs), 6, 146, 150-154 background, 148-150 benefits of tokenization, 154-155 capital requirements, 171-172 case studies, 156-161 challenges, 155-156 consultation outcomes, 173-176 general principles, 168-171 regulatory issues, 168-176 risks of permissionless DLTS and smart contracts, 161-168 Asset-pricing relationships comparison of cryptocurrency and equity market factors, 100-103 cryptocurrency pricing by equity and crypto factors, 104-108 cryptocurrency pricing by global and regional factors, 108-109 data, 98-100 Association of Proprietary Traders (APT), 174 Auto loans, 154 Automated teller machines (ATMs), 17

Central bank digital currencies (CBDCs) are been designed a ‘new normal’ for the world of finance in which payments (digital currency) can be made directly from one party to another without financial intermediaries regardless macroeconomic links. Digital currency has ushered the world of finance into uncharted waters as central banks and international institutions crumble to fine their feet’s in a fast moving stream of decentralised finance (DeFi) and global stablecoins. As central banks embark on the CBDCs journey, there are number of questions which must be address: What problems are CBDC’s expected to solve? How do current design thinking resolve these problems in Africa? Are there alternatives to CBDCs in Africa ? Broadly, CBDCs are expected to resolve inefficient and costly domestic and cross-border payments and settlement, ensure price stability and financial stability, retain monetary policy independence and digital de-dollarization. A qualitative descriptive design has been adopted in this study.Findings. Central bank digital currencies (CBDCs) well-designed in a new multilateral fair and just international monetary architecture has the potential to ensure price stability and financial stability in both advanced and developing economies in general, but more importantly would enable developing countries to regain some among of monetary policy independence. However, under the CBDCs design thinking within the framework of existing international monetary and financial architecture, no economy in Africa can withstand the powers of BigFintech, DeFi, global stablecoins and foreign sovereign digital currencies. This paper concludes that African countries must decide whether to cede their sovereign power to an independent monetary authority with single digital currency to manage under their control or cede their economic and financial destiny to unaccountable foreign BigFintech and/or foreign sovereign CBDCs in form of digital dollarization. Keywords: digital currency, CBDCs, digital dollarization, international monetary system, Africa DOI: 10.7176/RJFA/12-18-03 Publication date: September 30 th 2021

PurposeThe purpose of this study is to describe the present taxonomy of money, summarize potential central bank digital currency (CBDC) regimes that central banks worldwide could adopt and explore the implications of the introduction of each of these CDBC regimes for money laundering through the lens of the regulatory dialectic theory.Design/methodology/approachThe methodology used in the analysis of significant recent events regarding the progress of central banks in establishing a CBDC and the implications for money laundering under a CBDC regime. This paper also reviews the literature regarding the Regulatory Dialectic to highlight potential innovative responses of money launderers to circumvent the controls generated through the implementation of a CBDC.FindingsThis study examines the impact of Kane’s regulatory dialectic paradigm on the feasibility of money laundering under a CBDC regime and identifies potential avenues that would be available for those seeking to launder money, based on the form a CBDC would take.Research limitations/implicationsThis paper is unable as of yet to empirically evaluate anti-money laundering (AML) tactics under a CBDC regime as it has not yet been fully implemented.Practical implicationsMany central banks worldwide are evaluating the structure of and introduction of a CBDC. There are a number of forms that a CBDC could take, each of which has implications for individual privacy and for entities involved in AML efforts within financial institutions and the regulatory community. The paper has implications for AML experts who are considering how AML procedures would change under a CBDC regime.Social implicationsThe regulatory dialectic predicts that regulatory response reactive, rather than proactive when it comes to socially undesirable phenomena. As central banks and governments seek to divert economic activity away from the laundering of the proceeds of illicit activity, there are tradeoffs in terms of a loss of privacy. The regulatory dialectic predicts a corresponding innovative response of those who wish to undermine the controls generated through the establishment of a CBDC.Originality/valueTo the authors’ knowledge, this is the first paper to explore the impact of a potential CBDC on money laundering and the potential innovative circumventions within the paradigm of the Regulatory Dialectic.

Objectives: This work aims to contribute towards Tanzanian Central Bank Digital Currency (CBDC) users’ privacy preservation. It proposes the design of a privacy preserving CBDC which might be issued by Tanzania's Central Bank (CB), the Bank of Tanzania (BoT), which is currently in CBDC research phase. The work also aims to contribute to literature, the CBDC research being done by BoT, other CBs and CBDC stakeholders around the world. Methods: By using the Design Science Research (DSR) methodology, a privacy preserving CBDC design suitable for Tanzania was proposed, demonstrated and evaluated. This is the result of existing literature showing that different countries have different CBDC designs due to their differences in contexts and purposes for CBDC issuance. This consequently emphasized the fact that a CBDC design should not be treated as a one-size fits all solution. Findings: As opposed to the existing general and other country specific CBDC designs, we proposed a privacy preserving CBDC design suitable for Tanzania by consulting literature and taking into consideration the Tanzanian context. The design appears to be promising Tanzanian CBDC users’ privacy preservation though further work needs to be done. The work should not only be on practical evaluation of the proposed design but also on other factors impacting the success of CBDC projects. This will consequently further increase the success probability of CBDC projects, hence the potential for practical realization of CBDC project benefits. Novelty: Existing literature has shown that, considering the countries’ differences in context and CBDC issuance purposes, CBDC design should not be treated as a generic solution thereby obliging the need for country-specific CBDC designs. Consequently, the privacy preserving CBDC design suitable specifically for Tanzania consists of and provides an outline of privacy preserving interactions among the identified key Tanzanian CBDC participants or actors. The actors are the BoT, the intermediaries (i.e., other banks and payment service providers), Tanzania’s National Identification Authority (NIDA), financial transactions violation detection engine, and the expected CBDC users. Keywords: Digital currency, database privacy, central bank digital currency, privacy

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 introduction of Central Bank Digital Currency (CBDC) is a game-changer in the world of global money. Given the digital realm reforming monetary systems, central banks are getting keen on CBDCs to offer a state-backed alternative to private digital currencies and payment systems. This paper aims at carrying out a brief analysis of CBDCs and their impact on monetary sovereignty in view of globalization, technological disruption and rising decentralized finance. Central Bank Digital Currencies (CBDCs) hold promise for bolstering the state’s monetary policy. However, they also pose various challenges to the financial stability of states and cross-border payments involving CBDCs. The study also mentions the changing character of the role of central banks such as the Reserve Bank of India and compares international approaches like the digital yuan of China and the digital euro of the European Union. The paper concludes that CBDCs represent an instrument to strengthen monetary sovereignty as well as a catalyst to redefine monetary sovereignty in the digital age. INTRODUCTION The Money has changed from bartering to metallic coins, paper currency, and now digital currency. Cryptocurrencies like bitcoin have been rising in value at a rapid rate in recent years. As a result, the dollar value and stock markets have been challenged. Decentralized digital currencies trade without a central authority which threatens to undermine the sovereignty of money. #Centralization In this context, countries’ central banks around the world have started to investigate the Central Bank Digital Currency (CBDC). It is a digital form of the sovereign currency that the state issues and regulates. CBDCs are regulated by an authority unlike cryptocurrencies which are decentralized and do not have a backing of a central authority over them. In a rapidly digitalizing economy, states are striving to gain more control over the monetary and financial system.At the heart of this debate surrounding the eurozone members and their exceptional trade ties with Europe lies monetary sovereignty. The emergence of digital currencies private and state raises questions of the future of this sovereignty.The study aims to critically analyze the interrelationship between CBDCs and monetary sovereignty. The implementation of CBDCs allow the central banks to strengthen their control over the monetary authority and also reveal the risks it carry.

PurposeSeveral terms are interchangeably employed by researchers and practitioners to refer to central bank digital currency (CBDC), resulting in potential mistakes in the CBDC description. This study aims to survey the conceptualization of the CBDC and its utilization context to propose a list of CBDC terminologies.Design/methodology/approachThe research method used is the multivocal literature review, which covers the state-of-the-art with scientific papers and state-of-the-practice with practitioners' reports of the CBDC terminology.FindingsThe finding reveals that the terminologies used to mention a digital currency (DC) issued by a central bank are digital money, official DC, DC, centrally banked cryptocurrencies, digital cash, digital central bank money, CBDCs, central bank-issued cryptocurrency, central bank cryptocurrency, digital fiat currency, central bank-issued digital cash and sovereign digital currencies. The authors who proposed CBDC with distributed ledger technology-based infrastructure named it central bank cryptocurrency, and the others who didn’t specify clearly the infrastructure called it CBDC or another synonym of the DC.Originality/valueWe propose a CBDC concept map to clarify the CBDC understanding, which lists all terminologies found in the literature in a logical structure. The proposed CBDC concept map elucidates the linguistic landscape and clarifies the interpretation nuances across different CBDC terminologies, provides a comprehensive blueprint of the multi-conceptualization nature of CBDCs and contributes with an accessible tool for economists, technologists and lawyer researchers.

Central Bank Digital Currencies (CBDCs) garnered significant attention from economists, policymakers, and financial institutions worldwide. As a digital currency issued and managed by central banks, CBDCs are being explored by 130 countries, with 11 fully launched. The development of CBDCs has been accelerated by technological advancements and the global Covid-19 pandemic. Within the Organization of Islamic Countries (OIC), CBDC exploration varies in speed and interest among stakeholders. While most CBDC initiatives are still in the pilot phase, there is no universally agreed upon standardized format. This article evaluates the issue of CBDC from both Islamic law and economics perspectives by examining CBDCs' status as money in Islamic law and explores key aspects from the Islamic economics perspective, including monetary sovereignty, money market monitoring, and fiscal policies. Although CBDCs hold great potential, their successful implementation requires careful consideration of regulatory and technological challenges while adhering to Islamic principles. Integrating CBDCs into an Islamic economic framework offers an opportunity for sound economic development that aligns with Islamic values. Further research focusing on CBDC pilot projects and Islamic economic model designs is recommended to gain a deeper understanding of their implications.

This paper aims to examine the revolutionary impact of digital technologies—specifically blockchain systems and central bank digital currencies (CBDCs)—on modern financial architecture and the processes of global economic integration. These technologies are rapidly transforming the structure and functioning of both national and international financial systems, placing states, markets, and institutions before new opportunities and challenges within the contemporary economic landscape. Blockchain technology, as one of the core components of Industry 4.0, is widely applied in areas such as peer-to-peer (P2P) transactions, trade finance, smart contracts, digital asset tokenization, and data protection. This research highlights its influence on the functional structure of financial markets, particularly in the context of international payments, transaction transparency, and cybersecurity. Simultaneously, the study explores the evolving role of traditional financial institutions—especially central banks—in the digital age. In Georgia, the emergence of blockchain-based startups in areas like payments, digital contract management, and data security is already evident, positioning this technology as a potential driver of economic transformation in the country. CBDCs, as digital currencies issued by central banks and directed both toward the general public (retail CBDC) and financial institutions (wholesale CBDC), differ from other digital innovations by serving as a strategic instrument that bridges monetary policy, sovereign currency systems, and international financial relations. In addition to blockchain, this study analyzes the potential role of CBDCs in global economic integration—specifically how they support the optimization of cross-border payment systems, increase financial inclusion, and strengthen digital sovereignty, particularly for developing economies. Furthermore, it addresses the geopolitical and regulatory complexities that accompany the global implementation of these technologies. Methodologically, the research employs a mixed-methods approach. The impact of blockchain is evaluated using financial market indicators from Yahoo Finance and Bloomberg, as well as global digital governance indices. The analysis of CBDCs relies on documentary review, including reports from international organizations (e.g., IMF, BIS, World Bank), academic literature, and regulatory frameworks developed by central authorities. The paper is structured as follows: the first section discusses the theoretical and practical aspects of blockchain technology; the second section focuses on the technological and policy foundations of CBDCs; the third section examines their impact on areas such as international trade, financial policy, monetary independence, and cybersecurity. Finally, the case of Georgia is presented as an example of the combined influence of blockchain and CBDCs in an emerging economy. The study’s main conclusion demonstrates that the integration of blockchain and CBDCs is transforming the rules of the game in financial markets. The technological architecture is shifting to a new digital paradigm, where fast, low-cost, and secure transactions are replacing traditional financial intermediaries. Simultaneously, the role of central banks is being strengthened in monetary and credit policy, while their responsibilities regarding cybersecurity and data protection are also expanding. Successful implementation of CBDCs will significantly enhance both domestic financial stability and participation in global monetary relations—provided that international cooperation, legal frameworks, and technical standardization are effectively developed. Similarly, the application of blockchain technology—particularly in Georgia—requires a strategic vision and infrastructure support to harness its potential not only for improving financial products but also for fostering economic development and integration into the global system. Therefore, this paper confirms that the digital technology revolution—namely blockchain and CBDCs—represents not only a technological shift but a profound structural transformation in the global financial architecture, requiring integrated policy approaches and coordinated actions at both national and international levels. Keywords: Blockchain technologies, digital currency, central bank, financial markets, economic integration, Georgian economy, CBDC, international trade, monetary policy.

China, the United States, and Central Bank Digital Currencies: How Important Is It to Be First?

Supply chain management based on volatility clustering: The effect of CBDC volatility

Introduction/Main Objectives: Central Bank Digital Currency (CBDC) is an electronic form of banknotes, but different from virtual currency or cryptocurrency which are not issued by the state, the CBDC issued and guaranteed by the central bank. The aim of this study is to investigate the impacts of CBDC on financial stability using a Vector Auto-regressive model. The endogenous variables in the VAR estimation contain the Central Bank Digital Currency Attention Index (CBDCA), composite stock price index, real exchange rate, and interest rate (BI7DRR). Background Problems: The presence of CBDC will change the objective of Bank Indonesia and influence the structure of the monetary policy, which is no longer focused on achieving a low and stable inflation rate but on achieving price stability. Novelty: Although CBDCs will be launched worldwide, there are a limited number of empirical studies that have analyzed their impact on financial stability, especially for the case study in Indonesia. In this paper, we also use the Vector Error Correction Model (VECM) model with stochastic volatility and Impulse Response Function to make a forecast and see the impacts of shocks on the financial variables. Research Methods: In this study, we used monthly time series data from January 2019 – January 2023. In order to find the correlation between CBDC and the financial market we used the Granger causality model and impulse response function analysis. Finding/Results: The results of this study prove that CBDC has a positive correlation with the real exchange rate, and financial markets such as stock or bond prices have a positive response to shocks in CBDC. Conclusion: In this study, we used monthly time series data from January 2019 – January 2023. We use empirical tests to examine the CBDC attention index in relation to index attention, exchange rates, interest rates and IHSG. Our empirical results show that in Granger causality there is no causal relationship between CBDC and other macroeconomic variables. whereas in the IRF analysis, the response to the CBDC shock tends to be stagnant, the FEVD results show short-term and long-term shock fluctuations in the CBDC and other variables. These results indicate that CBDC does not have a significant effect on the macroeconomic variables used as indicators of financial system stability, but on the contrary, people's attention to CBDC depends on the condition of the variables in the financial system. On the other hand, the development of CBDC depends on economic conditions. The uncertainty surrounding CBDC plays an important role in indicating that the introduction of CBDC brings significant changes to the economy.

In a two-period decision model, a central bank chooses a CBDC (central bank digital currency) interest rate and a representative household allocates resources into production, consumption, CBDC holding, and non-CBDC holding. The model’s analytical results and a plausible benchmark are compared with the empirics for the US, China and Russia. Interesting novelties of the article are that the model predicts that the US in 2021/2022 should choose rather than 0.125% CBDC interest to combat its high October 2021 empirical inflation of 6.2%. That would induce households to hold more CBDC, hold less non-CBDC, and produce and consume less. In contrast, the model predicts that China should choose a low rather than CBDC interest rate. That would decrease each household’s CBDC holding and increase the low inflation. The model predicts that Russia should choose rather than CBDC interest rate. Russia’s strategy is remarkably consistent with the model’s predictions. The model predicts that the central bank should choose negative CBDC interest rate when the inflation and real interest rate are low, and the inflation target is high. The article shows how extremely high inflation, which increases the CBDC interest rate, makes production and consumption nearly impossible, unless the real interest rate is extremely negative.

This paper explores the impact of Central Bank Digital Currency (CBDC) volatility on the efficiency of monetary policy, focusing on its role in financial inclusion and investment. As the world increasingly embraces digital currencies, central banks are exploring the potential benefits and risks associated with CBDCs. Volatility in CBDCs may have significant implications for the effectiveness of monetary policy, potentially influencing inflation control, interest rates, and economic stability. Moreover, CBDCs' introduction could either enhance or hinder financial inclusion by providing new opportunities for unbanked populations or exacerbating existing disparities. The paper reviews existing literature on CBDCs, highlighting key findings on their volatility and its effects on policy formulation. Furthermore, it assesses how volatility may impact investment decisions, both in terms of risk perception and market behavior. By examining the intersection of monetary policy, financial inclusion, and investment, this paper aims to provide a comprehensive understanding of CBDCs' potential in shaping modern economies. It is critical for policymakers to consider the dynamic nature of CBDC volatility when designing strategies for effective financial inclusion and sustainable investment. The paper concludes by identifying gaps in the current literature and proposing future research directions to further explore CBDC's impact on global economic systems. Ultimately, this review underscores the importance of mitigating volatility to maximize CBDCs' benefits in fostering inclusive economic growth.

Central bank digital currency: A systematic literature review using text mining approach