2022/08/25 by Gayan K. Kulatilleke, Kulatilleke, Gayan K.
Business, Management and Accounting · Computer Science · Engineering · #Cryptography and Security (cs.CR) #Electricity Theft Detection Techniques #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2208.11900
openalex publication_date 2022/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising fraud percentages there is growing interest in finding appropriate machine learning classifiers for detection. However, fraud data sets are diverse and exhibit inconsistent characteristics. As a result, a model effective on a given data set is not guaranteed to perform on another. Further, the possibility of temporal drift in data patterns and characteristics over time is high. Additionally, fraud data has massive and varying imbalance. In this work, we evaluate sampling methods as a viable pre-processing mechanism to handle imbalance and propose a data-driven classifier selection strategy for characteristic highly imbalanced fraud detection data sets. The model derived based on our selection strategy surpasses peer models, whilst working in more realistic conditions, establishing the effectiveness of the strategy.