Hybrid Model Integrating Firefly Algorithm and Gradient Boosted Trees for Stronger Financial Fraud Detection
Class imbalance; F1 score; Financial fraud; Firefly Algorithm; Gradient Boosted Trees; Metaheuristic optimization; Precision; Real-time systems; Recall
Financial fraud poses a critical threat, with complex attack patterns overwhelming traditional rule-based detection methods. This project proposes a hybrid model integrating the Firefly Algorithm (FA) for hyperparameter optimization with Gradient Boosted Trees (GBTs) to enhance fraud detection. Evaluated on an imbalanced financial dataset, the FA-GBT framework achieved significant performance gains. ROC-AUC increased by 5.08% to 0.9883, demonstrating superior discrimination. Critically, Precision rose by 13.38%, substantially reducing false positives/false alarms and operational costs. The optimization also reduced training time by 64%(from 9.8 seconds to 3.5 seconds). This supports a robust, high-confidence, and efficient strategy for deployment in real-time transaction systems. 2025 IEEE.
Lobo, Nethan Noel; Rajesh, R.; Radhalakshmi, K.N.
Proceedings of the 9th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2025;pp.1964-1971
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICECA66444.2025.11383302 https://www.scopus.com/pages/publications/105034900857?origin=resultslist
Lobo N.N., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Rajesh R., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Radhalakshmi K.N., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India