Causal Inference for Banking Finance and Insurance A Survey

TL;DR

This survey reviews 37 papers from 1992-2023 on applying causal inference techniques to banking, finance, and insurance, highlighting current state and future research directions.

Contribution

It categorizes existing research, discusses key statistical methods, and identifies gaps, emphasizing the infancy of causal inference applications in these sectors.

Findings

Causal inference application in banking and insurance is still developing.

Various statistical methods like Bayesian networks and Granger causality are used.

Future research directions are identified for advancing the field.

Abstract

Causal Inference plays an significant role in explaining the decisions taken by statistical models and artificial intelligence models. Of late, this field started attracting the attention of researchers and practitioners alike. This paper presents a comprehensive survey of 37 papers published during 1992-2023 and concerning the application of causal inference to banking, finance, and insurance. The papers are categorized according to the following families of domains: (i) Banking, (ii) Finance and its subdomains such as corporate finance, governance finance including financial risk and financial policy, financial economics, and Behavioral finance, and (iii) Insurance. Further, the paper covers the primary ingredients of causal inference namely, statistical methods such as Bayesian Causal Network, Granger Causality and jargon used thereof such as counterfactuals. The review also recommends some important directions for future research. In conclusion, we observed that the application of causal inference in the banking and insurance sectors is still in its infancy, and thus more research is possible to turn it into a viable method.