Deep learning boosts detection of fraudulent financial transactions
Improving Financial Cybersecurity Through Fraud Transaction Detection with Deep Learning
Machine Learning
Summary
Catching financial fraud is hard because bad guys keep changing their tricks. The authors show how advanced computer programs called deep learning models can spot unusual money transactions better than older methods. They tested several types of these models on real data and found they catch more fraud and make fewer mistakes. This helps companies protect money more effectively and quickly.
What this means in practice
- •For financial security teams: Improve automated monitoring systems to detect and block fraudulent financial transactions more accurately with the proposed deep learning methods.
- •For banking software developers: Integrate tested deep learning models into fraud detection modules in financial software to enhance real-time detection capabilities.
Authors
Harish Kant Pathak, Rahul Kumar
Abstract
Financial fraud detection remains a critical challenge in cybersecurity, with traditional rule-based systems struggling to keep pace with evolving attack patterns. This paper presents a deep learning approach for fraud transaction detection, leveraging neural network architectures to identify anomalous financial behaviors with high accuracy. We evaluate multiple deep learning models including LSTM, CNN, and hybrid architectures on real-world financial transaction datasets, demonstrating significant improvements over conventional machine learning baselines. Our approach achieves robust performance in detecting fraudulent transactions while minimizing false positives, providing a scalable solution for enterprise financial cybersecurity systems