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Morphic Computing with Machine Learning for Enhanced Fraud Detection in Financial Applications


Affiliations
1 Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, India

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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.

Keywords

Morphic Computing, Machine Learning, Fraud Detection, Financial Security, Real-Time Detection
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  • Morphic Computing with Machine Learning for Enhanced Fraud Detection in Financial Applications

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Authors

N.P. Ponnuviji
Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, India
M. Vigilson Prem
Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, India

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.

Keywords


Morphic Computing, Machine Learning, Fraud Detection, Financial Security, Real-Time Detection