arXiv:2606.10393v1 Announce Type: new Abstract: Credit-card fraud detection is difficult because fraudulent transactions are rare, costly, and unevenly distributed. Strong gradient-boosted tree models already perform well on structured transaction data, so the value of another fusion method is not obvious. This paper examines whether Combinatorial Fusion Analysis (CFA), which searches over model subsets and rank-score fusion rules, can still add value on the IEEE-CIS Fraud Detection benchmark. Using a leakage-free 60/20/20 train/validation/test protocol, we evaluate 480 fusion configurations b
The continuous evolution of AI models for financial crime detection, particularly in response to more sophisticated fraud tactics, necessitates ongoing research into improving accuracy and efficiency.
Improving fraud detection directly impacts financial institutions' profitability and customer trust by reducing losses and enhancing security, which is a constant concern in the digital economy.
This research suggests a potential incremental improvement in credit-card fraud detection through advanced combinatorial fusion methods, offering better performance in imbalanced datasets.
- · Financial Institutions
- · AI/ML researchers
- · Fraud detection software providers
- · Credit card fraudsters
Reduced financial losses for banks and credit card companies due to more effective fraud identification.
Increased consumer confidence in using digital payment methods due to enhanced security.
Potential for similar combinatorial fusion techniques to be applied to other imbalanced datasets in various industries beyond finance.
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Read at arXiv cs.LG