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.