Blockchain and Machine Learning Integration for Real-Time Fraud Detection in Fintech
DOI:
https://doi.org/10.56472/ICCSAIML25-160Keywords:
Blockchain Technology, Machine Learning, Fraud Detection, Fintech Security, Real-Time Processing, Decentralized Ledger, Predictive AnalyticsAbstract
In the rapidly evolving financial sector, fraudulent activities pose significant threats to the integrity and security of transactions. As digital payment systems and online financial services continue to grow, the volume and complexity of financial data make traditional fraud detection methods increasingly inadequate. This paper explores the integration of blockchain technology with machine learning (ML) to develop a robust, scalable system for real-time fraud detection in fintech applications. Blockchain’s decentralized and immutable ledger ensures that all financial transactions are recorded transparently and securely, minimizing the risks of data tampering and unauthorized access. Meanwhile, ML algorithms—including supervised, unsupervised, and reinforcement learning techniques—are applied to historical and real-time transaction data to identify anomalies and patterns indicative of fraudulent behavior. The synergy between blockchain and machine learning offers several advantages. Blockchain provides a reliable data source that ML models can trust, while ML enhances the utility of blockchain by enabling intelligent monitoring and predictive analysis. The proposed system architecture incorporates smart contracts to automate enforcement of security policies and ML-driven anomaly detection to respond to suspicious activities as they occur. Case studies and experimental results demonstrate the effectiveness of the hybrid approach, showing improved detection rates, reduced false positives, and faster response times compared to conventional systems. By combining the security and transparency of blockchain with the adaptive intelligence of machine learning, this research contributes a novel framework for safeguarding financial ecosystems. The proposed system not only enhances the accuracy and efficiency of fraud detection mechanisms but also builds a more trustworthy and resilient infrastructure for digital financial transactions
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