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.

翻译：因果推断在解释统计模型和人工智能模型做出的决策中发挥着重要作用。近年来，这一领域开始吸引研究者和从业者的关注。本文对1992年至2023年间发表的37篇关于因果推断在银行、金融和保险领域应用的论文进行了全面综述。这些论文按以下领域分类：(i) 银行业，(ii) 金融及其子领域，如公司金融、治理金融（包括金融风险与金融政策）、金融经济学和行为金融学，以及(iii) 保险。此外，本文涵盖了因果推断的主要组成部分，即统计方法（如贝叶斯因果网络、格兰杰因果检验）及其相关术语（如反事实）。本综述还指出了未来研究的一些重要方向。总结而言，我们发现因果推断在银行和保险领域的应用仍处于初级阶段，因此有更多研究可探索其作为可行方法的潜力。