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ISSN: 2582-8266 (Online)  || UGC Compliant Journal || Google Indexed || Impact Factor: 9.48 || Crossref DOI

Fast Publication within 2 days || Low Article Processing charges || Peer reviewed and Referred Journal

Research and review articles are invited for publication in Volume 18, Issue 2 (February 2026).... Submit articles

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

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

Jaydeep Taralkar *

Capitol University, USA.

Review Article

World Journal of Advanced Engineering Technology and Sciences, 2025, 15(02), 454-461

Article DOI: 10.30574/wjaets.2025.15.2.0354

DOI url: https://doi.org/10.30574/wjaets.2025.15.2.0354

Received on 17 March 2025; revised on 27 April 2025; accepted on 30 April 2025

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. 

Financial Fraud Detection; Deep Learning Anomaly Detection; Self-Adaptive Learning; Real-Time Transaction Monitoring; Distributed Computing Architecture

https://wjaets.com/sites/default/files/fulltext_pdf/WJAETS-2025-0354.pdf

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Jaydeep Taralkar. FinAI: Deep learning for real-time anomaly detection in financial transactions. World Journal of Advanced Engineering Technology and Sciences, 2025, 15(02), 454-461. Article DOI: https://doi.org/10.30574/wjaets.2025.15.2.0354.

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