A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection

TL;DR

This paper presents an explainable, data-driven case-based reasoning system for financial risk detection that offers interpretability and competitive prediction performance without requiring extensive domain expertise.

Contribution

It introduces a novel automatic design CBR system that enhances interpretability and prediction accuracy in financial risk detection, reducing reliance on prior domain knowledge.

Findings

The CBR system provides natural economic interpretations of results.

It achieves comparable or better prediction performance than other AI methods.

The approach enhances transparency in financial risk forecasting.

Abstract

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.