Morphic computing with machine learning for enhanced fraud detection in financial applications (Record no. 22705)
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250424105141.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250424b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25969 |
| Author | Ponnuviji, N. P. |
| 245 ## - TITLE STATEMENT | |
| Title | Morphic computing with machine learning for enhanced fraud detection in financial applications |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.15(2), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Mumbai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3545-3550p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | As financial fraud becomes increasingly complex, traditional detection<br/>methods struggle to keep pace, resulting in substantial financial losses<br/>globally. Morphic computing—a paradigm that emphasizes adaptable,<br/>context-aware processing—offers promising advancements for fraud<br/>detection in dynamic environments. Integrating morphic computing<br/>with machine learning models creates a responsive framework capable<br/>of discerning subtle and evolving fraud patterns. The proposed system<br/>utilizes a Convolutional Neural Network (CNN) enhanced with<br/>Morphic Layering, where layers adaptively morph in response to new<br/>data patterns. The dataset, sourced from real-time financial<br/>transactions, consists of 500,000 records, including 2,000 flagged<br/>fraudulent cases. The system was tested on a simulated environment<br/>over a six-month period, yielding an accuracy of 98.5% in fraud<br/>detection and reducing false positives by 40% compared to traditional<br/>machine learning models. Latency for real-time detection was<br/>minimized to 200 milliseconds, proving feasible for immediate<br/>application in transaction monitoring systems. By offering a flexible<br/>structure, this method surpasses existing approaches, as it continuously<br/>evolves to detect emerging fraud patterns, thus enhancing financial<br/>security. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25970 |
| Co-Author | Vigilson Prem, M. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Chennai ICT Academy |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_10_3545_3550.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 24/04/2025 | 2025-0654 | 24/04/2025 | 24/04/2025 | Articles Abstract Database |