Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction

Andrés Alonso-Robisco, José Manuel Carbó

Financial Innovation 2022 article cited by 77

Source Open-access PDF More in Business & Industry

importance 5/10 ai assisted Financial Distress and Bankruptcy Prediction banking / credit risk stage: research

Problem

ML algorithms improve credit default prediction but introduce new model risks, and uncertainty about how supervisors assess these risks may block innovation.

Why it matters

Model risk affects regulatory capital, credit scoring, and provisioning decisions in banking.

What the AI did

A framework quantifies model risk-adjustments to compare ML methods, identifying 13 risk components in 3 categories weighted by intensity of mentions in regulatory documents.

Results

not stated in source

Impact

Offers a supervisory-oriented framework for comparing ML credit models, per abstract.

Record

Venue	Financial Innovation
Published	2022
Work type	article
Authors	Andrés Alonso-Robisco, José Manuel Carbó
Citations	77 (OpenAlex, at import time)
References	58
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1186/s40854-022-00366-1
Open access	diamond
Keywords	machine learning, credit default, model risk, regulatory validation, supervisory review

Importance rating

5 / 10

real but narrower advance

Model risk in ML credit prediction affects regulatory capital, credit scoring, and provisioning in banking, but abstract gives no scale; framework is a supervisory comparison tool, a real but narrower advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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