{"title":"A Comprehensive Review of Causal Inference in Banking, Finance, and Insurance","authors":"Satyam Kumar, Yelleti Vivek, Vadlamani Ravi, Indranil Bose","doi":"10.1145/3736752","DOIUrl":null,"url":null,"abstract":"This is a comprehensive survey of the applications of causal inference in the Banking, Financial Services and Insurance (BFSI) domain based on 45 papers published from 1992 to 2023. It categorizes papers into (i) Banking and risk management (ii) Finance (covering investment, asset and portfolio management; behavioral finance and time series), (iii) Financial markets and (iv) Insurance. Exploring methods such as Bayesian Causal Network, Granger Causality, and counterfactuals, the paper emphasizes significance of causal inference in explaining predictions of AI/ML models. This survey also recommends promising future research directions in the intersection of causal inference and these domains making it helpful for the professionals working therein.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"21 1","pages":""},"PeriodicalIF":23.8000,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3736752","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
This is a comprehensive survey of the applications of causal inference in the Banking, Financial Services and Insurance (BFSI) domain based on 45 papers published from 1992 to 2023. It categorizes papers into (i) Banking and risk management (ii) Finance (covering investment, asset and portfolio management; behavioral finance and time series), (iii) Financial markets and (iv) Insurance. Exploring methods such as Bayesian Causal Network, Granger Causality, and counterfactuals, the paper emphasizes significance of causal inference in explaining predictions of AI/ML models. This survey also recommends promising future research directions in the intersection of causal inference and these domains making it helpful for the professionals working therein.
期刊介绍:
ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods.
ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.