FinAI: Deep learning for real-time anomaly detection in financial transactions

Taralkar, Jaydeep (2025) FinAI: Deep learning for real-time anomaly detection in financial transactions. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 454-461. ISSN 2582-8266

FinAI represents a groundbreaking deep learning framework designed to address the critical challenges of financial fraud detection in today's high-volume digital transaction environment. Traditional rule-based detection systems have proven increasingly inadequate against sophisticated fraud techniques, suffering from high false positive rates and delayed processing times. The FinAI solution integrates stream processing technologies with advanced neural network architectures on Cloudera's distributed computing platform to enable real-time anomaly detection across multiple transaction channels. Its three-tiered architecture—comprising a Stream Processing Layer using Apache Kafka and Spark, a specialized Deep Learning Engine, and a Self-Adaptive Learning Module—delivers substantial improvements in detection accuracy, processing efficiency, and operational cost reduction. Through continuous learning mechanisms that adapt to evolving fraud patterns, FinAI maintains exceptional performance while minimizing false alerts, fundamentally transforming fraud management economics for financial institutions worldwide.