Machine learning explainability in finance: an application to default risk analysis
Anupam Datta (),
Carsten Jung () and
Shayak Sen ()
Additional contact information
Anupam Datta: Carnegie Mellon University
Carsten Jung: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
Shayak Sen: Carnegie Mellon University
No 816, Bank of England working papers from Bank of England
We propose a framework for addressing the ‘black box’ problem present in some Machine Learning (ML) applications. We implement our approach by using the Quantitative Input Influence (QII) method of Datta et al (2016) in a real‑world example: a ML model to predict mortgage defaults. This method investigates the inputs and outputs of the model, but not its inner workings. It measures feature influences by intervening on inputs and estimating their Shapley values, representing the features’ average marginal contributions over all possible feature combinations. This method estimates key drivers of mortgage defaults such as the loan‑to‑value ratio and current interest rate, which are in line with the findings of the economics and finance literature. However, given the non‑linearity of ML model, explanations vary significantly for different groups of loans. We use clustering methods to arrive at groups of explanations for different areas of the input space. Finally, we conduct simulations on data that the model has not been trained or tested on. Our main contribution is to develop a systematic analytical framework that could be used for approaching explainability questions in real world financial applications. We conclude though that notable model uncertainties do remain which stakeholders ought to be aware of.
Keywords: Machine learning; explainability; mortgage defaults (search for similar items in EconPapers)
JEL-codes: C55 G21 (search for similar items in EconPapers)
Pages: 44 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-gth
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0816
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