Explainable AI for Financial Risk Management

Bowden, James and Cummins, Mark and Dao, Daniel and Jain, Kushagra (2024) Explainable AI for Financial Risk Management. University of Strathclyde, Glasgow.

[thumbnail of FRIL-WPS-2024-Explainable-AI-for-financial-risk-management]
Preview
 Text. Filename: FRIL-WPS-2024-Explainable-AI-for-financial-risk-management.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

We overview the opportunities that Explainable AI (XAI) offer to enhance financial risk management practice, which feeds into the objective of simplifying compliance for banking and financial services organisations. We provide a clear problem statement, which makes the case for explainability around AI systems from the business and the regulatory perspective. A comprehensive literature review positions the study and informs the solution framework proposed. The solution framework sets out the key considerations of an organisation in terms of setting strategic priorities around the explainability of AI systems, the institution of appropriate model governance structures, the technical considerations in XAI analytics, and the imperative to evaluate explanations. The use case demonstration brings the XAI discussion to life through an application to AI based credit risk management, with focus on credit default prediction.

ORCID iDs

Bowden, James ORCID logoORCID: https://orcid.org/0000-0002-0419-1882, Cummins, Mark ORCID logoORCID: https://orcid.org/0000-0002-3539-8843, Dao, Daniel ORCID logoORCID: https://orcid.org/0000-0001-9449-8003 and Jain, Kushagra;

Persistent Identifier

https://doi.org/10.17868/strath.00089573
CORE (COnnecting REpositories)