Causal Inference for Banking Finance and Insurance A Survey
Satyam Kumar Yelleti Vivek Vadlamani Ravi Indranil Bose
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.