AI-Driven Optical Character Recognition for Fraud Detection in FinTech Income Verification Systems

This paper discusses some aspects of the application of Artificial Intelligence to fraud detection in real-time income verification systems of the FinTech industry. Stressing the advanced level of Computer Vision-based Optical Character Recognition, this study demonstrates how financial data extraction and analysis have been accurately performed from documents such as bank statements and pay slips by AI algorithms, while detecting anomalies and discrepancies indicative of fraud. The proposed framework therefore contributes to enhancing efficiency in fraud detection with lesser false positives.This will integrate AI-driven anomaly detection models, including supervised and unsupervised machine learning techniques, to provide fraud identification in real time with unparalleled precision and responsiveness to novel fraudulent patterns. The outcome reveals that this would optimize the transaction security and scalability, solving a very critical limitation of the classic systems. This study showed how AI is set to transform a more robust, efficient, and trustworthy FinTech income verification ecosystem.