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Cybersecurity UDOMFinGuard-X: A Trust-Aware, Privacy-Preserving and Quantum-Resistant Framework for Cross-Bank Fraud Detection
Problem Statement
Financial fraud can involve activities spread across multiple banks. A fraudster may deliberately distribute transactions between different financial institutions so that each individual bank sees only part of the suspicious activity. As a result, important fraud patterns may only become visible when information from multiple institutions is considered together. However, banks cannot simply share their customers' raw transaction data because financial transaction information is sensitive and must be protected. This creates a security challenge: banks need to collaborate in detecting fraud while protecting customer privacy. There is also a future cybersecurity concern because commonly used public-key cryptographic systems may face risks from powerful quantum computers. Therefore, financial institutions need approaches that improve collaborative fraud detection while also protecting sensitive information and securing communication against future threats.
Proposed Solution
FinGuard-X proposes a collaborative cross-bank fraud detection framework that allows participating banks to train a shared fraud-detection model without directly sharing their raw customer transaction data. Each bank keeps its transaction data locally and uses Federated Learning to train a local fraud-detection model. Instead of sending raw transaction records to a central system, each bank sends only protected model updates. The system uses Trust-Aware Robust Aggregation to identify and reduce the influence of suspicious or compromised participants. Differential Privacy and Secure Aggregation are used to provide additional protection for the shared model updates. For secure communication, the framework proposes Post-Quantum Cryptography using ML-KEM for secure key establishment and ML-DSA for digital signatures. The proposed approach therefore combines fraud detection, privacy protection, trust management, and quantum-resistant cybersecurity in one framework.
Innovation
The main innovation of FinGuard-X is the combination of several cybersecurity and emerging technologies to solve the problem of cross-bank fraud detection without requiring banks to share raw customer transaction data. The concept combines: -Federated Learning for collaborative fraud detection while keeping data locally. -Trust-Aware Robust Aggregation to reduce the effect of malicious or compromised participants. -Differential Privacy and Secure Aggregation to strengthen privacy protection. -Post-Quantum Cryptography to protect communication against current and future cryptographic threats. Instead of creating another centralized system that collects financial data from different banks, FinGuard-X focuses on secure collaboration while keeping sensitive data within each participating institution.
Expected Impact
-Improve the ability of financial institutions to identify fraud patterns that cross institutional boundaries. Reduce the need to share raw customer transaction data. -Strengthen privacy protection during collaborative fraud detection. -Improve resilience against malicious or compromised participants. -Strengthen the security of communication between participating institutions. -Prepare financial institutions for future quantum-related cryptographic threats. -Support faster and more informed fraud investigation. -Contribute to stronger protection of customers and financial institutions against coordinated fraud.