AI-Driven Enterprise Data Integrity and Fraud Detection Frameworks for Secure Financial and Cryptocurrency Transactions

Abstract

The rapid growth of digital finance, online banking, blockchain platforms, and cryptocurrency ecosystems has transformed global financial transactions. While these innovations provide enhanced efficiency, accessibility, and transparency, they also introduce significant challenges related to data integrity, fraud detection, cybersecurity, and regulatory compliance. Traditional fraud prevention mechanisms often struggle to identify sophisticated attacks, money laundering schemes, identity theft, and anomalous transaction patterns occurring across distributed financial networks. Artificial Intelligence (AI) has emerged as a powerful solution for strengthening enterprise data integrity and improving fraud detection capabilities through machine learning, deep learning, predictive analytics, and real-time monitoring technologies. This study proposes an AI-driven framework designed to ensure data integrity and detect fraudulent activities in financial and cryptocurrency transaction environments. The framework integrates intelligent anomaly detection, behavioral analytics, blockchain validation, risk scoring, and automated compliance monitoring to create a secure transaction ecosystem. Furthermore, the research examines how AI algorithms can continuously evaluate transaction patterns, identify suspicious behavior, and protect sensitive financial information from cyber threats. The findings suggest that combining AI technologies with enterprise governance and blockchain-based validation mechanisms significantly enhances transaction security, operational transparency, regulatory compliance, and customer trust. The proposed framework offers organizations a scalable and adaptive approach to managing emerging risks in modern financial and cryptocurrency systems.