ItemConference Paper

Multilevel CNN Based Hybrid Framework for Adaptive Credit Card Fraud Detection

Title
Multilevel CNN Based Hybrid Framework for Adaptive Credit Card Fraud Detection
Creator
Lalitha, P.; Nooka Raju, Guntu; Sindhu, V.; Sujatha, Capt. Kambhampati; Mandal, Pawan; Arulkarthick, E.K.
Description
Credit card fraud presents a substantial problem to financial organizations, as fast changing fraudulent activities necessitate advanced detection techniques. Conventional machine learning methods frequently encounter challenges with adaptability and precision in imbalanced datasets. This study presents a multilevel CNN-based hybrid architecture that combines deep convolutional networks with traditional ensemble classifiers for adaptive credit card fraud detection. The platform includes an adaptive learning module that facilitates ongoing model upgrades, guaranteeing responsiveness to emerging fraud trends. The system, evaluated using a benchmark Kaggle dataset, attained an accuracy of 99.48%, precision of 98.76%, recall of 99.05%, F1-score of 98.90%, and AUC-ROC of 99.91%, outperforming established baseline models such as Logistic Regression, Random Forest, and XGBoost. The suggested system's capacity to integrate deep feature extraction with hybrid classifiers yields enhanced detection efficiency, reduced false positives, and improved generalization. This research enhances fraud detection by overcoming the constraints of static models, rendering it applicable for real-time financial applications and adaptable to emerging threats. 2025 IEEE.
Source
2025 IEEE 2nd International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Adaptive Learning; Anomaly Detection; Credit Card Faud Detection; Deep Learning; Ensemble Methods; Feature Extraction; Financial Security; Hybrid Classifiers; Imbalanced Data; Incremental Learning; Multilevel Convolutional Neural Network; Real-Time Fraud Detection
Coverage
Lalitha P., Geetanjali College of Engineering and Technology, Department of Computer Science and Engineering, Hyderabad, India; Nooka Raju G., GMR Institute of Technology, Department of Electronics and Communication Engineering, Rajam, Andhra Pradesh, Vizianagaram, India; Sindhu V., Christ University, Department of Computer Science, Karnataka, India; Sujatha C.K., St. Joseph's College for Women(A), Department of Mathematics, Andhra Pradesh, Visakhapatnam, India; Mandal P., RIMT University, Department of Forensic Science, Punjab, Mandi Gobindgarh, India; Arulkarthick E.K., Nandha Engineering College, Department of Electronics and Communication Engineering, Tamil Nadu, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833150784-8;
Format
online
Language
English
Type
Conference paper

Citation

Lalitha, P.; Nooka Raju, Guntu; Sindhu, V.; Sujatha, Capt. Kambhampati; Mandal, Pawan; Arulkarthick, E.K., “Multilevel CNN Based Hybrid Framework for Adaptive Credit Card Fraud Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26051.

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