# Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection

arXiv: https://arxiv.org/abs/2312.13334 · HTML: https://mlsanity.com/paper/2312.13334/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range](https://mlsanity.com/paper/1407.8038/) (2014)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection](https://mlsanity.com/paper/2310.07354/) (2023)
- [Histopathological Image Classification and Vulnerability Analysis using Federated Learning](https://mlsanity.com/paper/2310.07380/) (2023)

## Built on by (most cited first, 13 of 13)

- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [Prospects of Privacy Advantage in Quantum Machine Learning](https://mlsanity.com/paper/2405.08801/) (2024)
- [Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions](https://mlsanity.com/paper/2503.10058/) (2025)
- [Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods](https://mlsanity.com/paper/2505.10050/) (2025)
- [Shapley Value-Guided Adaptive Ensemble Learning for Explainable Financial Fraud Detection with U.S. Regulatory Compliance Validation](https://mlsanity.com/paper/2604.14231/) (2026)
- [Transforming Triple-Entry Accounting with Machine Learning: A Path to Enhanced Transparency Through Analytics](https://mlsanity.com/paper/2411.15190/) (2024)
- [XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing](https://mlsanity.com/paper/2503.12435/) (2025)
- [Machine learning for fraud detection in digital banking: a systematic literature review REVIEW](https://mlsanity.com/paper/2510.05167/) (2025)
- [Synapse: Federated Tool Routing via Typed Compendium Artifacts](https://mlsanity.com/paper/2602.00911/) (2026)
- [U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition](https://mlsanity.com/paper/2602.16749/) (2026)
- [Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challenges](https://mlsanity.com/paper/2607.13045/) (2026)
- [Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details](https://mlsanity.com/paper/2609.15722/) (2026)
- [Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos](https://mlsanity.com/paper/2609.33754/) (2026)

## Abstract (arXiv)

Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for financial institutions, but some factors make the development of effective and efficient fraud detection systems a challenge. One of such factors is the fact that fraudulent transactions are rare and that many transaction datasets are imbalanced; that is, there are fewer significant samples of fraudulent transactions than legitimate ones. This data imbalance can affect the performance or reliability of the fraud detection model. Moreover, due to the data privacy laws that all financial institutions are subject to follow, sharing customer data to facilitate a higher-performing centralized model is impossible. Furthermore, the fraud detection technique should be transparent so that it does not affect the user experience. Hence, this research introduces a novel approach using Federated Learning (FL) and Explainable AI (XAI) to address these challenges. FL enables financial institutions to collaboratively train a model to detect fraudulent transactions without directly sharing customer data, thereby preserving data privacy and confidentiality. Meanwhile, the integration of XAI ensures that the predictions made by the model can be understood and interpreted by human experts, adding a layer of transparency and trust to the system. Experimental results, based on realistic transaction datasets, reveal that the FL-based fraud detection system consistently demonstrates high performance metrics. This study grounds FL's potential as an effective and privacy-preserving tool in the fight against fraud.