Beyond the Leaderboard: A Drift-Aware, Cost-Sensitive Evaluation Protocol for Machine Learning in Financial Fraud Detection

Sai Rakshit Yerram

doi:10.36948/ijfmr.2026.v08i03.80155

Beyond the Leaderboard: A Drift-Aware, Cost-Sensitive Evaluation Protocol for Machine Learning in Financial Fraud Detection

Author(s)	Mr. Sai Rakshit Yerram
Country	United States
Abstract	Machine learning has become central to financial fraud detection, yet a growing body of evidence indicates that the way such models are evaluated in the academic literature diverges sharply from the conditions under which they are deployed. Reported gains are frequently driven by methodological artefacts, such as random train and test splitting of temporally ordered transactions, resampling applied before splitting, and reliance on accuracy or area under the receiver operating characteristic curve, rather than by genuine improvements in fraud-catching capability. This article argues that the field suffers from a benchmark-to-deployment gap and proposes a unified evaluation protocol intended to close it. The protocol rests on four principles: strictly temporal data partitioning that forbids the use of future information, simulation of realistic label delay to reflect the months-long interval before fraud is confirmed, cost-sensitive and operationally meaningful metrics aligned with the economics of false positives and false negatives, and full reporting of the conditions under which results are obtained. The protocol is positioned against the datasets and practices that dominate current research, and a re-evaluation methodology is described whereby previously published models are reassessed under realistic conditions. It is shown that conventional evaluation can inflate apparent performance substantially and can reorder the relative ranking of methods, so that models presented as state of the art under random splitting may underperform simpler baselines once temporal integrity and cost are respected. The article concludes that adopting a shared, deployment-aware evaluation protocol is a precondition for meaningful scientific progress in fraud detection and offers a concrete, reproducible specification to that end.
Keywords	Financial Fraud Detection, Evaluation Protocol, Concept Drift, Cost-sensitive Learning, Temporal Data Leakage, Delayed Labels, Reproducibility, Benchmark Methodology
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-06
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.80155

View / Download PDF File

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Beyond the Leaderboard: A Drift-Aware, Cost-Sensitive Evaluation Protocol for Machine Learning in Financial Fraud Detection

Share this