Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection (2312.13334v1)

Abstract: 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.

• The paper introduces a novel framework combining federated learning and explainable AI to enhance fraud detection while safeguarding sensitive financial data.
• It utilizes a decentralized deep neural network with federated averaging and SHAP visualizations to offer clear, interpretable insights into model predictions.
• Results demonstrate a robust 93% accuracy with balanced precision, recall, and F1-score, underlining the framework’s effectiveness in secure fraud detection.

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

The paper explores the integration of Federated Learning (FL) and Explainable AI (XAI) in detecting financial fraud, addressing the core issues of data privacy, model transparency, and robustness in fraud detection systems.

Background and Motivation

Financial fraud detection has historically been a complex issue due to the evolving nature of fraudulent activities and their infrequency compared to legitimate transactions. Traditional ML models often fall short due to data privacy laws that prevent institutions from sharing customer data, thus limiting the potential for a highly accurate centralized model.

The research leverages FL, a decentralized ML approach that allows financial institutions to collaboratively train models without the need to share sensitive data. Complementing FL, XAI is integrated to provide transparency, enabling human experts to interpret model predictions and build trust in automated fraud detection systems.

Methodology

Federated Learning Architecture

The FL architecture consists of a central server that coordinates model training and aggregation across multiple banks. Each bank trains a local model on its private dataset and shares only the model updates with the server. This workflow preserves data privacy and ensures that sensitive information remains in situ.

Figure 1: Workflow of the proposed System.

The system employs a Deep Neural Network (DNN) for detecting fraudulent activities across federated datasets. The model undergoes periodic updates, averaged by the server using the Federated Averaging algorithm. By doing so, the global model benefits from the diverse data patterns distributed across various entities while ensuring the security and confidentiality of the data.

Explainable AI Integration

Incorporating XAI into the FL model is essential to understand and trust the model's decisions. SHAP (SHapley Additive exPlanations) values are employed to attribute the model's predictions to its features, offering insights into model behavior and helping identify which features significantly influence fraud detection.

import shap explainer = shap.Explainer(global_model) shap_values = explainer(X) shap.summary_plot(shap_values, X)

Implementation Details

The implementation emphasizes robust data preprocessing and feature selection to enhance model accuracy. The imbalance inherent in the dataset is addressed using SMOTE to ensure equitable class representation.

Key implementation steps include:

1. Data Processing: Numeric and categorical data imputation is performed, followed by outlier removal using the IQR method.
2. Feature Engineering: Techniques like binning and one-hot encoding are applied to make the model training more effective.
3. Model Development: A three-layer DNN with ReLU activations is used, optimized using the Adam optimizer and evaluated with binary cross-entropy.
4. Performance Metrics: Evaluated using accuracy, precision, recall, and F1-score, specific metrics reflect the model's competence in handling class imbalances.

Results and Evaluation

Performance Metrics

The FL model demonstrates robust performance with accuracy reaching 93%. The precision, recall, and F1-score metrics complement each other, revealing a well-balanced performance across different fraud detection scenarios.

Explainability Insights

The incorporation of SHAP plots provides transparency in model predictions by illustrating feature importance and impact on the output. For example, features that exhibited high importance provided clear indicators of fraudulent transactions, thus allowing experts to understand and trust the model's decisions.

Figure 2: SHAP Plot of Client 1.

Conclusion

The paper establishes a novel framework that successfully integrates FL and XAI for detecting financial fraud. This approach not only enhances privacy through decentralized model training but also assures transparency in model predictions via XAI techniques. Future research may focus on further refining the global model's convergence speed and efficiency, as well as exploring additional applications in other sectors where data privacy and interpretability are critical.