and cash flows of businesses active in their e-commerce space, thereby extremely reducing the cost of collecting information. Using MLanalysis, they could therefore process the creditworthiness of borrowers without requiring professional financial reports, which is often cited as a big burden for small borrowers. Furthermore, they can make cashflow-based loans without requiring collateral, another major hindrance for small companies trying to obtain credit from banks ( BIS 2019 ).
Similarly, big tech companies that offer payment services to consumers can leverage
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Based on technical assistance to central banks by the IMF’s Monetary and Capital Markets Department and Information Technology Department, this paper examines fintech and the related area of cybersecurity from the perspective of central bank risk management. The paper draws on findings from the IMF Article IV Database, selected FSAP and country cases, and gives examples of central bank risks related to fintech and cybersecurity. The paper highlights that fintech- and cybersecurity-related risks for central banks should be addressed by operationalizing sound internal risk management by establishing and strengthening an integrated risk management approach throughout the organization, including a dedicated risk management unit, ongoing sensitizing and training of Board members and staff, clear reporting lines, assessing cyber resilience and security posture, and tying risk management into strategic planning.. Given the fast-evolving nature of such risks, central banks could make use of timely and regular inputs from external experts.
, occupational fraud, and to support fraud audits;
3) Detection front office behaviors, and observe emerging behavioral patterns to predict latent risks, and detect links between employees;
4) Detection of money laundering by analyzing large datasets;
5) Control of operational risks by using effective Workflow Management;
6) Analyzing the best ways to protect systems through AI/MLanalysis;
7) Process-automation to accelerate the pace of routine tasks, minimize human error, and make processes in general more efficient and more secure;
8) Setting-up of early
regional MLanalysis and research reports to raise awareness of and provide policy guidance to LEAs and other competent authorities on new trends and typologies. C.29.4(b) requires strategic analysis to use available and obtainable information, including data that may be provided by other competent authorities, to identify ML- and TF-related trends and patterns. The limitations presented by the stand-alone databases at the level of the 36 PBC provincial branches and the limited access by branches to CAMLMAC’s database, as set out above, also negatively affect
This report provides a summary of the anti-money laundering/combating the financing of terrorism (AML/CFT) measures in place in the People’s Republic of China (China)1 as at the date of the onsite visit (July 9–27, 2018). It analyzes the level of compliance with the Financial Action Task Force (FATF) 40 Recommendations and the level of effectiveness of China’s AML/CFT system and provides recommendations on how the system could be strengthened. China has undertaken a number of initiatives since 2002 that have contributed positively to its understanding of ML/TF risk, although some important gaps remain. Its framework for domestic AML/CFT cooperation and coordination is well established.