BIS Paper Examines How Non-Traditional Data Affect Credit Scoring
BIS published a working paper that examines how machine learning and non-traditional data affect credit scoring. The paper compares the predictive power of credit scoring models based on machine learning techniques, as used by fintech companies, with that of traditional loss and default models typically used by banks. The results show that the model based on machine learning and non-traditional data used by the fintech company is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply.
Using proprietary transaction-level data from a leading fintech company in China for the period between May and September 2017, the authors tested the performance of different models to predict losses and defaults, both in normal times and when the economy is subject to a shock. They analyzed the case of an (exogenous) change in regulation policy on shadow banking in China that caused lending to decline and credit conditions to deteriorate. The main conclusions of the paper can be summarized as follows:
- The machine learning-based credit scoring models outperform traditional empirical models (using both traditional and non-traditional information) in predicting borrowers’ losses and defaults.
- Non-traditional information improves the predictive power of the model.
- While the models perform similarly well in normal times, the model based on machine learning is better able to predict losses and defaults following a negative shock to the aggregate credit supply. One possible reason for this is that machine learning can better mine the non-linear relationship between variables in the event of a shock.
- The predictive power of all the models improves when the length of the relationship between bank and customer increases. However, the comparative advantage of the model that uses the fintech credit scoring technique based on machine learning tends to decline when the length of the relationship increases.
Related Links
Keywords: International, China, Banking, Machine Learning, Credit Scoring, Fintech, Credit Risk, Big Data, BIS
Related Articles
EBA Proposes Standards for IRRBB Reporting Under Basel Framework
The European Banking Authority (EBA) proposed implementing technical standards on the interest rate risk in the banking book (IRRBB) reporting requirements, with the comment period ending on May 02, 2023.
FED Issues Further Details on Pilot Climate Scenario Analysis Exercise
The U.S. Federal Reserve Board (FED) set out details of the pilot climate scenario analysis exercise to be conducted among the six largest U.S. bank holding companies.
US Agencies Issue Several Regulatory and Reporting Updates
The Board of Governors of the Federal Reserve System (FED) adopted the final rule on Adjustable Interest Rate (LIBOR) Act.
ECB Issues Multiple Reports and Regulatory Updates for Banks
The European Central Bank (ECB) published an updated list of supervised entities, a report on the supervision of less significant institutions (LSIs), a statement on macro-prudential policy.
HKMA Keeps List of D-SIBs Unchanged, Makes Other Announcements
The Hong Kong Monetary Authority (HKMA) published a circular on the prudential treatment of crypto-asset exposures, an update on the status of transition to new interest rate benchmarks.
EU Issues FAQs on Taxonomy Regulation, Rules Under CRD, FICOD and SFDR
The European Commission (EC) adopted the standards addressing supervisory reporting of risk concentrations and intra-group transactions, benchmarking of internal approaches, and authorization of credit institutions.
CBIRC Revises Measures on Corporate Governance Supervision
The China Banking and Insurance Regulatory Commission (CBIRC) issued rules to manage the risk of off-balance sheet business of commercial banks and rules on corporate governance of financial institutions.
HKMA Publications Address Sustainability Issues in Financial Sector
The Hong Kong Monetary Authority (HKMA) made announcements to address sustainability issues in the financial sector.
EBA Updates Address Basel and NPL Requirements for Banks
The European Banking Authority (EBA) published regulatory standards on identification of a group of connected clients (GCC) as well as updated the lists of identified financial conglomerates.
ESMA Publishes 2022 ESEF XBRL Taxonomy and Conformance Suite
The General Board of the European Systemic Risk Board (ESRB), at its December meeting, issued an updated risk assessment via the quarterly risk dashboard and held discussions on key policy priorities to address the systemic risks in the European Union.