Explainable AI in Credit Risk Management

16 Pages Posted: 29 Mar 2021

See all articles by Branka Hadji Misheva

Branka Hadji Misheva

Zurich University of Applied Sciences

Ali Hirsa

Columbia University

Joerg Osterrieder

University of Twente; Bern Business School

Onkar Kulkarni

affiliation not provided to SSRN

Stephen Fung Lin

affiliation not provided to SSRN

Date Written: March 1, 2021

Abstract

Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance customer experience, democratize financial services, ensure consumer protection and significantly improve risk management. While it is easier than ever to run state-of-the-art machine learning models, designing and implementing systems that support real-world finance applications have been challenging. In large part because they lack transparency and explainability which are important factors in establishing reliable technology and the research on this topic with a specific focus on applications in credit risk management. In this paper, we implement two advanced post-hoc model agnostic explainability techniques called Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to machine learning (ML)-based credit scoring models applied to the open-access dataset offered by the US-based P2P Lending Platform, Lending Club. Specifically, we use LIME to explain instances locally and SHAP to get both local and global explanations. We discuss the results in detail and present multiple comparison scenarios by using various kernels available for explaining graphs generated using SHAP values. We also discuss the practical challenges associated with the implementation of these state-of-art eXplainabale AI (XAI) methods and document them for future reference. We have made an effort to document every technical aspect of this research, while at the same time providing a general summary of the conclusions.

Keywords: Explainable AI, Credit Lending, Machine Learning, LIME, SHAP

Suggested Citation

Hadji Misheva, Branka and Hirsa, Ali and Osterrieder, Joerg and Kulkarni, Onkar and Fung Lin, Stephen, Explainable AI in Credit Risk Management (March 1, 2021). Available at SSRN: https://ssrn.com/abstract=3795322 or http://dx.doi.org/10.2139/ssrn.3795322

Branka Hadji Misheva (Contact Author)

Zurich University of Applied Sciences ( email )

IDP
Technikumstrasse 9
Winterthur, CH 8401
Switzerland

Ali Hirsa

Columbia University ( email )

500 West 120th Street
New York, NY 10027

HOME PAGE: http://www.ieor.columbia.edu/faculty/ali-hirsa

Joerg Osterrieder

University of Twente ( email )

Drienerlolaan 5
Departement of High-Tech Business and Entrepreneur
Enschede, 7522 NB
Netherlands

Bern Business School ( email )

Brückengasse
Institute of Applied Data Sciences and Finance
Bern, BE 3005
Switzerland

Onkar Kulkarni

affiliation not provided to SSRN

Stephen Fung Lin

affiliation not provided to SSRN

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