AI-DRIVEN FRAUD DETECTION AND SECURITY SOLUTIONS: ENHANCING ACCURACY IN FINANCIAL SYSTEMS

Financial institutions worldwide are facing a major challenge in identifying unauthorized transactions. Financial organizations need sophisticated fraud detection systems to safeguard their financial stability and maintain customer confidence; nevertheless, the design of such systems may be hindered by many complexities. The infrequency of illegal transactions and the disproportion in several transaction datasets, namely the few occurrences of fraudulent transactions relative to legitimate ones, exemplify these characteristics. The dataset disparity may undermine the accuracy and effectiveness of a fraudulent activity detection program. Disseminating customer information to provide a more effective centralized approach is impractical, since each bank is required to comply with data protection standards. To ensure the user experience remains unaltered, the fraud detection solution must be both accessible and easily observable. As a result, this study presents an innovative approach in tackling these issues by combining Federated Learning (FL) with Explainable AI (XAI). FL financial institutions work together to train a model that can identify fraudulent activities without sharing client information directly. This protects the privacy and security of client information. The addition of XAI makes it definite that human specialists can understand and analyse the model's results, which gives the system more reliability and transparency. The FL-based fraudulent detection system regularly shows high-performance measures, as shown by experiments employing real transaction datasets. This study demonstrates that federated learning might serve as an effective and confidential instrument in combating fraudulent activity.