Abstract

As financial fraud becomes increasingly complex, traditional detection methods struggle to keep pace, resulting in substantial financial losses globally. Morphic computing—a paradigm that emphasizes adaptable, context-aware processing—offers promising advancements for fraud detection in dynamic environments. Integrating morphic computing with machine learning models creates a responsive framework capable of discerning subtle and evolving fraud patterns. The proposed system utilizes a Convolutional Neural Network (CNN) enhanced with Morphic Layering, where layers adaptively morph in response to new data patterns. The dataset, sourced from real-time financial transactions, consists of 500,000 records, including 2,000 flagged fraudulent cases. The system was tested on a simulated environment over a six-month period, yielding an accuracy of 98.5% in fraud detection and reducing false positives by 40% compared to traditional machine learning models. Latency for real-time detection was minimized to 200 milliseconds, proving feasible for immediate application in transaction monitoring systems. By offering a flexible structure, this method surpasses existing approaches, as it continuously evolves to detect emerging fraud patterns, thus enhancing financial security.