Cost-sensitive metaheuristic technique for credit card fraud detection. Issue 6 (17th August 2020)

• Record Type: Journal Article
• Authors: Singh, Ajeet; Jain, Anurag
• Is Part Of: Journal of information & optimization sciences. Volume 41:Issue 6(2020)
• Page Start: 1319
• Page End: 1331
• Publication Date: 2020-08-17
• Subjects: 68M25; Cost-sensitive learning -- Flower pollination algorithm -- Credit card fraud -- Metaheuristic -- Imbalanced data; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2020.1809090
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: To prevent online financial losses, credit card frauds need to be addressed and it should be controlled. To minimize the credit card frauds, fraudulent credit card transactions should be identified and minimized. Keeping this fact in view, in this paper, a cost-sensitive metaheuristic technique (CSFPA: cost-sensitive learning flower pollination metaheuristic algorithm) is proposed to minimize the misclassification cost of credit card transactions from class imbalance data using the correlation-based feature section method (CFS), flower pollination algorithm (FPA), and cost-sensitive classifier. To identify the fraudulent transactions, the random forest (RF) ensemble classifier is used as a base learner in the cost-sensitive classifier for classification tasks and the proposed CSFPA technique has been tested on the Brazilian bank dataset. The performance of proposed CSFPA has been compared with cost insensitive techniques and cost-sensitive techniques such as NB (Naive Base), KNN (K-Nearest Neighbors), LB (Logit Boost), CSForest (Cost-sensitive decision forest), NBBFS (NB Best First Search) KNNGSS (KNN Greedy Stepwise Search), and CSFCS (CSForest Cuckoo Search). The experimental outcomes revealed that the proposed CSFPA technique has obtained encouraging outcomes for handling misclassification costs (Total cost and Avg. cost) and also has outperformed (FPR, Precision and Recall) compared to all the other approaches.