IMF published a working paper on application of machine learning in assessing credit risk. This paper reviews the underlying challenges in assessing credit risk of particularly small borrowers and discusses the most prominent machine learning techniques applied in assessing credit risk of borrowers for a nontechnical audience.
The paper examines potential strengths and weaknesses of machine-learning-based credit assessment by presenting core ideas and the most common techniques in machine learning for the nontechnical audience; it also discusses the fundamental challenges in credit risk analysis. The paper presents the main elements of prudent lending in the context of the five Cs of credit and agency problems. The five Cs of credit are capacity, capital structure, coverage, character, and conditions. The paper also discusses main machine learning tools and techniques and examines the strengths and weaknesses of machine learning-based lending in contrast with the traditional methods.
The paper highlights that machine learning has certain weaknesses that should be taken into consideration when applying for credit risk assessment:
- Heavy reliance on learning from data, particularly in a context where the size of the sample is considerably larger than traditional ways of credit scoring, could result in noisy information playing a role in driving results of credit analysis and leading to financial exclusion of creditworthy applicants. The bias in the sample should be identified and avoided by analysts as much as possible to avoid digital financial exclusion.
- Machine learning may not capture structural changes in a timely manner, because arrival of informative data may be slow due to the lengthy process of default observation. This could negatively impact fintech lenders that rely on machine learning to assess borrowers without evaluating the relevance of data used for training the model for new applicants.
- Borrowers may realize and counterfeit certain indicators that drive their credit score, thus decreasing the relevance of those features for new applicants.
- Machine learning is exposed to some of the key concerns in econometrics, most importantly the endogeneity and selection bias problem. The analyst should check the sample to ensure proper treatment of these issues and avoid superfluous results. Nonetheless, proper choice of risk drivers makes these issues less of a concern for credit risk assessment and default outcome prediction.
Overall, the paper concludes that fintech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by leveraging nontraditional data sources to improve the assessment of the borrower’s track record, appraising collateral value, forecasting income prospects, and predicting changes in general conditions. Nevertheless, the emerging market economies may face some challenges in reaping the benefits while ensuring that the development of fintech credit does not expose systemic risks to the financial system. Given the central role of data in machine-learning-based credit analysis, it should be legally and technologically possible to gather digitalized data reliably from various sources and avoid noisy and biased data as much as possible. As a complement for high-quality data availability, cyber-security measures should be in place because of the sensitivity of credit information. Moreover, the machine learning analysts should consistently review machine learning models for credit rating to avoid potential weaknesses of naive application of machine learning.
Related Link: Working Paper
Keywords: International, Banking, Financial Inclusion, Fintech, Machine Learning, Credit Risk, Big Data, Systemic Risk, IMF
Across 35 years in banking, Blake has gained deep insights into the inner working of this sector. Over the last two decades, Blake has been an Operating Committee member, leading teams and executing strategies in Credit and Enterprise Risk as well as Line of Business. His focus over this time has been primarily Commercial/Corporate with particular emphasis on CRE. Blake has spent most of his career with large and mid-size banks. Blake joined Moody’s Analytics in 2021 after leading the transformation of the credit approval and reporting process at a $25 billion bank.
Previous ArticleIMF Paper Examines Issues in Calibration of CCyB Under Basel III
APRA issued a letter on the loss-absorbing capacity (LAC) requirements for domestic systemically important banks (D-SIBs) and published a discussion paper, along with the proposed the prudential standards on financial contingency planning (CPS 190) and resolution planning (CPS 900).
The European Commission (EC) launched a call for evidence, until March 18, 2022, as part of a comprehensive review of the macro-prudential rules for the banking sector under the Capital Requirements Regulation (CRR) and Directive (CRD IV).
The Financial Stability Board (FSB) published a report that sets out good practices for crisis management groups.
The Australian Prudential Regulation Authority (APRA) found that Heritage Bank Limited had incorrectly reported capital because of weaknesses in operational risk and compliance frameworks, although the bank did not breach minimum prudential capital ratios at any point and remains well-capitalized.
The Office of the Superintendent of Financial Institutions (OSFI) released the annual report for 2020-2021.
Through a letter addressed to the banking sector entities, the Office of the Superintendent of Financial Institutions (OSFI) announced deferral of the domestic implementation of the final Basel III reforms from the first to the second quarter of 2023.
EIOPA recently published a letter in which EC is informing the European Parliament and Council that it could not adopt the set of draft regulatory technical standards for disclosures under the Sustainable Finance Disclosure Regulation (SFDR) within the stipulated three-month period, given their length and technical detail.
The Financial Conduct Authority (FCA) published the third in a series of policy statements that set out rules to introduce the UK Investment Firm Prudential Regime (IFPR), which will take effect on January 01, 2022.
The Australian Prudential Regulation Authority (APRA) published, along with a summary of its response to the consultation feedback, an information paper that summarizes the finalized capital framework that is in line with the internationally agreed Basel III requirements for banks.
The Committee on Payments and Market Infrastructures (CPMI) and the International Organization of Securities Commissions (IOSCO) issued a consultative report focusing on access to central counterparty (CCP) clearing and client-position portability.