# Causal Inference for Banking Finance and Insurance A Survey

arXiv: https://arxiv.org/abs/2307.16427 · HTML: https://mlsanity.com/paper/2307.16427/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 81 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [Interpretable Credit Application Predictions With Counterfactual Explanations](https://mlsanity.com/paper/1811.05245/) (2018)
- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification](https://mlsanity.com/paper/2010.12512/) (2020)
- [Causality Learning: A New Perspective for Interpretable Machine Learning](https://mlsanity.com/paper/2006.16789/) (2020)
- [Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components](https://mlsanity.com/paper/1411.3972/) (2014)
- [Learning Interpretable Models with Causal Guarantees](https://mlsanity.com/paper/1901.08576/) (2019)
- [Counterfactual Explanations Using Optimization With Constraint Learning](https://mlsanity.com/paper/2209.10997/) (2022)
- [Causal Analysis of Customer Churn Using Deep Learning](https://mlsanity.com/paper/2304.10604/) (2023)
- [Non-Parametric Causality Detection: An Application to Social Media and Financial Data](https://mlsanity.com/paper/1601.03610/) (2016)
- [Causal Data Science for Financial Stress Testing](https://mlsanity.com/paper/1703.03076/) (2017)

## Built on by (most cited first, 5 of 5)

- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder](https://mlsanity.com/paper/2503.01290/) (2025)
- [ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI](https://mlsanity.com/paper/2512.00839/) (2025)
- [The Estimation of Continual Causal Effect for Dataset Shifting Streams](https://mlsanity.com/paper/2504.20471/) (2025)
- [An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making](https://mlsanity.com/paper/2609.30396/) (2026)

## Abstract (arXiv)

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.