Featured Product

    IMF Paper Examines Application of Machine Learning in Assessing Credit

    May 17, 2019

    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

    Featured Experts
    Related Articles
    News

    APRA Revises Standard on Margin Rules for Uncleared Derivatives

    APRA revised CPS 226, which is the prudential standard on margin and risk mitigation requirements for non-centrally cleared derivatives.

    September 19, 2019 WebPage Regulatory News
    News

    PRA Issues Consultation on Prudent Person Principle Under Solvency II

    PRA, via the consultation paper CP22/19, has set out its proposed expectations for investment by firms, in accordance with the Prudent Person Principle (PPP).

    September 18, 2019 WebPage Regulatory News
    News

    EIOPA Forms Consultative Expert Group on Digital Ethics in Insurance

    EIOPA established the Consultative Expert Group on Digital Ethics in Insurance to assist EIOPA in the development of digital responsibility principles in insurance.

    September 17, 2019 WebPage Regulatory News
    News

    FDIC Approves Proposal to Amend Swap Margin Rule

    FDIC approved what would be a joint proposal by the US Agencies (FCA, FDIC, FED, FHFA, and OCC) to amend regulations that require swap dealers and security-based swap dealers under the agencies’ respective jurisdictions to exchange margin with their counterparties for swaps that are not centrally cleared (Swap Margin Rule).

    September 17, 2019 WebPage Regulatory News
    News

    FASB Proposes Taxonomy Changes Related to Topics 848 and 470

    FASB proposed taxonomy improvements for the proposed Accounting Standards Update on topic 848 on facilitation of effects of reference rate reform on financial reporting.

    September 16, 2019 WebPage Regulatory News
    News

    BoE Statement on Recalculating Transitional Measures Under Solvency II

    BoE notified that it will be willing to accept applications from firms to recalculate transitional measure on technical provisions (TMTP) as at September 30, 2019.

    September 16, 2019 WebPage Regulatory News
    News

    BIS Hosts Conference to Discuss Issues from Emergence of Stablecoins

    BIS hosted a conference in Basel to discuss policy and regulatory issues posed by the emergence of stablecoin initiatives backed by financial institutions and large technology companies.

    September 16, 2019 WebPage Regulatory News
    News

    BIS Paper on Embedded Supervision of Blockchain-Based Financial Market

    BIS published a working paper that investigates ways to regulate and supervise blockchain-based financial markets.

    September 16, 2019 WebPage Regulatory News
    News

    BoE Paper on Market-Implied Systemic Risk and Shadow Capital Adequacy

    BoE published a working paper that presents a forward-looking approach to measure systemic solvency risk.

    September 13, 2019 WebPage Regulatory News
    News

    HKMA Consults on Policy Module on Pillar 2 Supervisory Review Process

    HKMA is consulting on the revised Supervisory Policy Manual module CA-G-5 that sets out the HKMA approach to conducting the supervisory review process under Pillar 2.

    September 13, 2019 WebPage Regulatory News
    RESULTS 1 - 10 OF 3830