Featured Product

    BoE Paper on Predicting Bank Distress in UK Using Machine Learning

    October 04, 2019

    BoE published a working paper on predicting bank distress in the UK using machine learning techniques. In the analysis, the main input variables come from confidential regulatory returns while the measure of distress is derived from supervisory assessments of bank riskiness from 2006 through to 2012. Overall, the paper demonstrates practical benefits of machine learning and ensembling methods for providing regulators with advance warning of firm distress. Supervisors can apply these findings to aid in anticipating problems before they occur, thus helping them in their mission to keep financial institutions safe and sound.

    The authors compare a number of machine learning and classical statistical techniques, implementing a rigorous, double-block randomized cross-validation procedure to evaluate out-of-sample performance. The random forest algorithm was found to be superior in terms of ranking test observations, while also having relatively better calibrated probabilities than the other techniques. The performance results indicate that the random forest should be used to build an early warning system. To improve the transparency of the algorithm, the study examined the drivers of the predicted probabilities of the model, utilizing an aggregation of Shapley values per test set observation and Shapley regression framework. The Shapley regression reveals the importance of macroeconomic variables and a firm’s sensitivity to market risk, capital buffer, and net interest margin. Finally, the authors also performed simple ensembling techniques to combine all the model outputs, demonstrating substantive and statistically significant improvements relative to the random forest on its own.

    Future research might extend this analysis in a number of ways. First, scholars might seek to incorporate additional data beyond financial ratios and macroeconomic variables. Second, future work might delve into more complex configurations of diverse underlying models to reap substantive improvements. Third, the analysis relies on data from a highly unusual period in economic history. Future research might seek to establish whether the documented relationship between input variables and measures of distress persist in relatively benign economic environments. It is likely that in such periods macroeconomic variables are less important in predicting firm distress and, therefore, an early warning system might be better if it were based on data that encompasses more or all of an economic cycle.


    Related Links

    Keywords: Europe, UK, Banking, Machine Learning, Statistical Techniques, Ensembling Techniques, Research, Technology, Bank Distress, BoE

    Related Articles

    EBA Finalizes Templates for One-Off Climate Risk Scenario Analysis

    The European Banking Authority (EBA) has published the final templates, and the associated guidance, for collecting climate-related data for the one-off Fit-for-55 climate risk scenario analysis.

    November 28, 2023 WebPage Regulatory News

    EBA Mulls Inclusion of Environmental & Social Risks to Pillar 1 Rules

    The European Banking Authority (EBA) recently published a report that recommends enhancements to the Pillar 1 framework, under the prudential rules, to capture environmental and social risks.

    October 31, 2023 WebPage Regulatory News

    BCBS Consults on Disclosure of Crypto-Asset Exposures of Banks

    As a follow on from its prudential standard on the treatment of crypto-asset exposures, the Basel Committee on Banking Supervision (BCBS) proposed disclosure requirements for crypto-asset exposures of banks.

    October 19, 2023 WebPage Regulatory News

    BCBS and EBA Publish Results of Basel III Monitoring Exercise

    The Basel Committee on Banking Supervision (BCBS) and the European Banking Authority (EBA) have published results of the Basel III monitoring exercise.

    October 18, 2023 WebPage Regulatory News

    PRA Updates Timeline for Final Basel III Rules, Issues Other Updates

    The Prudential Regulation Authority (PRA) recently issued a few regulatory updates for banks, with the updated Basel implementation timelines being the key among them.

    October 18, 2023 WebPage Regulatory News

    US Treasury Sets Out Principles for Net-Zero Financing

    The U.S. Department of the Treasury has recently set out the principles for net-zero financing and investment.

    October 17, 2023 WebPage Regulatory News

    EC Launches Survey on G7 Principles on Generative AI

    The European Commission (EC) launched a stakeholder survey on the draft International Guiding Principles for organizations developing advanced artificial intelligence (AI) systems.

    October 14, 2023 WebPage Regulatory News

    ISSB Sustainability Standards Expected to Become Global Baseline

    The finalization of the two sustainability disclosure standards—IFRS S1 and IFRS S2—is expected to be a significant step forward in the harmonization of sustainability disclosures worldwide.

    September 18, 2023 WebPage Regulatory News

    IOSCO, BIS, and FSB to Intensify Focus on Decentralized Finance

    Decentralized finance (DeFi) is expected to increase in prominence, finding traction in use cases such as lending, trading, and investing, without the intermediation of traditional financial institutions.

    September 18, 2023 WebPage Regulatory News

    BCBS Assesses NSFR and Large Exposures Rules in US

    The Basel Committee on Banking Supervision (BCBS) published reports that assessed the overall implementation of the net stable funding ratio (NSFR) and the large exposures rules in the U.S.

    September 14, 2023 WebPage Regulatory News
    RESULTS 1 - 10 OF 8938