Development of advanced fraud detection systems using pattern recognition and anomaly detection

Posted: Oct 02, 2022

• Abigail Rodriguez
• Aiden Lee
• Aiden Roberts

Abstract

Financial fraud represents an escalating challenge in the digital economy, with global losses estimated to exceed $40 billion annually. Traditional fraud detection systems predominantly rely on rule-based approaches and conventional machine learning algorithms that often fail to adapt to the sophisticated and evolving nature of fraudulent activities. The limitations of existing systems include high false positive rates, computational inefficiency, and inability to detect novel fraud patterns in real-time. This research addresses these challenges through the development of an innovative hybrid framework that combines quantum-inspired pattern recognition with bio-inspired anomaly detection techniques. The novelty of our approach lies in the integration of principles from quantum computing and swarm intelligence, creating a system that can process financial transaction data with unprecedented efficiency and accuracy. Unlike conventional systems that analyze transactions sequentially, our quantum-entropy pattern recognition engine leverages superposition principles to evaluate multiple potential fraud patterns simultaneously. This parallel processing capability enables the system to identify complex, multi-dimensional fraud signatures that would remain undetectable to traditional approaches.