---
title: Use of AI and ML in Banking & Finance to Improve Decision-Making, Automate Processes, and Enhance Customer Experiences
url: https://www.ml-quant.com/papers/ssrn/5086625/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5086625
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5086625
featured: 2025-01-08
citations: 5
topic: Risk, Credit & Banking
---


# Use of AI and ML in Banking & Finance to Improve Decision-Making, Automate Processes, and Enhance Customer Experiences

The manuscript discusses the potential of AI and ML in finance and regulatory compliance, but also points out challenges related to data privacy, algorithmic bias, and model explainability.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5086625
- Identifier: SSRN 5086625
- Released: 2025-01-08
- First featured: Quant Letter No. 81 (2025-01-08): https://www.ml-quant.com/issues/2025-01-08/
- Citations (Semantic Scholar): 5
- Published in: not yet
- Topic: Risk, Credit & Banking

## Related

- [Measuring Bank Complexity Using Xai](https://www.ml-quant.com/papers/ssrn/4785689/): A machine learning technique shows a link between the complexity and opacity of banks, with complex firms seeing decreased trading activity.
- [XAI Framework for Risk Management](https://www.ml-quant.com/papers/repec/wsi-wschap-9781800615212-0004/): The article highlights the difficulties of using machine learning models in practical risk management in banking due to their opacity and lack of explainability, and introduces a framework for leading eXplainable AI methods.
- [Machine and Deep Learning for Credit Scoring: A compliant approach](https://www.ml-quant.com/papers/arxiv/2412.20225/): The research proposes new BASEL 2 and 3 compliant techniques for credit scoring in banks, demonstrating improved performance and default capture rate with Gradient Boosting Machines.
- [Predicting Bank Distress in Europe: Using Machine Learning and a Novel Definition of Distress](https://www.ml-quant.com/papers/ssrn/5098026/): The paper presents a machine learning-based early warning system to predict distress in large European banks, with the random forest model performing best.
- [Reciprocity in Interbank Markets](https://www.ml-quant.com/papers/arxiv/2412.10329/): The research explores the interdependence of banks in financial networks, revealing that smaller banks withdrew from high-value trades during the financial crisis.
- [Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework](https://www.ml-quant.com/papers/arxiv/2502.14479/): A study comparing three loan behavior modeling techniques finds multinomial logistic regression to be the most effective, potentially improving loss reserve estimates in banking.
