000			a
999			_c22705 _d22705
003			OSt
005			20250424105141.0
008			250424b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_925969 _aPonnuviji, N. P.
245			_aMorphic computing with machine learning for enhanced fraud detection in financial applications
250			_aVol.15(2), Oct
260			_aMumbai _bICT Academy _c2024
300			_a3545-3550p.
520			_aAs 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.
650		0	_94622 _aComputer Engineering
700			_925970 _aVigilson Prem, M.
773	0		_dChennai ICT Academy _tICTACT Journal on Soft Computing (IJSC)
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_10_3545_3550.pdf _yClick here
942			_2ddc _cAR