Fraud Detection Systems Using Deep Learning and Behavioral Intelligence

Fraud Detection Systems Using Deep Learning and Behavioral Intelligence: The rapid adoption of e-commerce technology has produced unprecedented improvements in lifestyle and convenience of purchasing. Over the years the mechanisms for shopping have also been improved and automated for the customers' benefit. In the backend however, contending for such highly streamlined technological improvement are threats and vulnerabilities attacking the financial pillars of such systems. Regular security production systems handle detection based on rules or heuristics defined by the historical behaviour, these systems have very little or no impact against a new strike. Moreover these fraud production systems handling digital transactions typically operate based on known malicious signatures or detect distinctive patterns. But the unparalleled rapid shift in banking especially the digital economy has outgrown the surge of fraud detection. As new-age fraudsters employ more sophisticated approaches to attack, companies need to adapt new digital fraud detection techniques to counter these imminent frauds. Both enterprises and academia have started exploring detection methods underpinned by concepts like behavioural intelligence and deep learning with a major focus on supervised learning techniques. Such techniques can indeed enhance detection efficiency, nevertheless when done in isolation they fall short in addressing the critical imbalance and the data privacy issues. Hence a novel crime prevention system taking a combined approach with only supervised, semi-supervised or unsupervised learning invariably the core challenge of model drift during the operationalisation stage invariably determines the success or failure of the fraud detection. The performance metrics used to evaluate the proposed system predict false positives with grave importance since fraudsters usually stage a fraudulent attempt when they know for sure that the system is less likely to detect their attempt. With increased focus on the balancing ,trait-level feature engineering, real-time inference, model drift, monitoring and scalability gives directions for future research in fraud detection systems.