Morphic computing with machine learning for enhanced fraud detection in financial applications
Ponnuviji, N. P.
Morphic computing with machine learning for enhanced fraud detection in financial applications - Vol.15(2), Oct - Mumbai ICT Academy 2024 - 3545-3550p.
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.
Computer Engineering
Morphic computing with machine learning for enhanced fraud detection in financial applications - Vol.15(2), Oct - Mumbai ICT Academy 2024 - 3545-3550p.
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.
Computer Engineering