Financial Fraud Detection Using Explainable AI and Federated Learning

Abstract

Financial fraud is a growing concern that threatens the integrity of financial institutions and customer trust. Traditional fraud detection methods, which rely on rule-based systems and centralized machine learning models, often struggle to keep up with evolving fraudulent tactics. Additionally, the black-box nature of many machine learning models limits their interpretability, making it difficult for financial analysts and regulatory bodies to trust and validate fraud detection outcomes. To address these challenges, Explainable AI (XAI) enhances model transparency by providing human-understandable explanations for fraud predictions, while Federated Learning (FL) enables privacy-preserving, collaborative model training across multiple institutions without sharing sensitive data.Federated Learning offers a decentralized approach that allows financial institutions to train fraud detection models on diverse, distributed datasets while ensuring compliance with data protection regulations. This improves model generalization and robustness by leveraging insights from various sources without compromising customer privacy. At the same time, XAI ensures that these models remain interpretable, helping analysts understand the reasoning behind fraud alerts, identify potential biases, and refine detection strategies accordingly.The combination of XAI and FL enables institutions to strengthen fraud detection capabilities while adhering to ethical AI practices and regulatory requirements. The integration of Explainable AI and Federated Learning in financial fraud detection offers a promising solution to the challenges of transparency and privacy. XAI improves the interpretability of fraud detection models, making them more accountable and understandable for stakeholders, while FL facilitates secure and efficient model training across different organizations. This paper explores the synergy between these technologies, discussing their advantages, challenges, and potential applications in enhancing fraud detection.The Combination of Federated Learning (FL) and Explainable AI (XAI) delivers a powerful solution for financial fraud detection offering strong privacy guarantees, improved model performance and enhanced transparency. This approach supports regulatory compliance and fosters confidence among stakeholders in the deployment of AI driven fraud prevention.

Introduction

1. Introduction

Financial fraud detection is becoming increasingly challenging due to the rise in digital transactions. Traditional centralized machine learning models raise concerns around data privacy, security, and regulatory compliance.

To address these issues:

• Federated Learning (FL) allows collaborative model training across institutions without sharing sensitive data.

• Explainable AI (XAI) adds transparency and interpretability to the models.

This combination improves fraud detection accuracy, security, and trustworthiness.

2. Related Work

Several studies have explored using FL and XAI for fraud detection:

• 2020: FL on edge devices safeguarded user data but faced issues with communication and inconsistent data quality.

• 2021: XGBoost + SHAP improved interpretability but was limited by centralized data.

• 2022: A hybrid FL-XAI model using LIME and SHAP showed strong results but was complex to deploy.

• 2023: A secure multi-institution FL system improved rare fraud pattern detection but struggled with data heterogeneity and model consistency.

3. Proposed System

The proposed system combines Federated Learning with Explainable AI to detect fraud in a privacy-preserving, transparent, and scalable manner.

Key Features:

• Each financial institution trains a local model on its own data.

• Encrypted updates are sent to a central server for aggregation using Federated Averaging.

• The global model is redistributed for further refinement.

• Explainable AI tools (e.g., SHAP, LIME) provide insights into predictions.

• A feedback loop helps continuously improve the system based on new fraud patterns.

Technical Steps:

• Data preprocessing and encoding

• Class balancing using SMOTE

• Model training and FL aggregation

• Application of XAI for interpretation

• System deployment with dashboards and classification reports

4. Results

• Visualization tools (correlation plots, fraud distribution)

• Functional UI: Home and Registration pages

• Classification metrics showing strong fraud detection performance

Conclusion

This study demonstrates the effectiveness of integrating Federated Learning (FL) and Explainable AI (XAI) in enhancing financial fraud detection systems. By leveraging FL, financial institutions can collaboratively train robust fraud detection models without compromising customer data privacy, thereby adhering to stringent data protection regulations. The incorporation of XAI provides critical transparency, enabling human experts to comprehend and trust the model\'s decision-making processes. Our findings indicate that the FL-based approach not only maintains high accuracy in identifying fraudulent transactions but also ensures that the system operates within the bounds of privacy-preserving protocols. This dual focus on privacy and transparency positions the proposed framework as a viable and innovative solution for modern financial institutions grappling with the complexities of fraud detection.

References

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Copyright

Copyright © 2025 Mrs. D. Aswani, Sriram Parashuram , Ganagani Sai Rithvik Goud, N Sai Krishna , Alluri Manohar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.