Threat Detection in FinTech Platforms Using Deep Learning and Cryptographic Safeguards

Abstract

FinTech platforms process a massive volume of transactions and user interactions in real time, making them prime targets for cyber threats, fraud, and data breaches. Traditional signature-based or rule-based security systems often fail to detect novel and adaptive attacks. This paper proposes a framework for threat detection in FinTech platforms by integrating deep learning models with cryptographic safeguards. Deep learning models, including LSTM and CNN architectures, monitor transactional behavior and system logs to identify anomalies indicative of fraud or security breaches. Cryptographic safeguards, such as end-to-end encryption, secure multiparty computation, and digital signatures, protect sensitive financial data during processing and transmission. Experimental evaluation demonstrates that the combined approach achieves high detection accuracy, low false positive rates, and strong data confidentiality, highlighting its suitability for modern, high-throughput FinTech environments.