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An intelligent payment card fraud detection system.

Manjeevan Seera¹, Chee Peng Lim², Ajay Kumar³

¹Econometrics and Business Statistics, School of Business, Monash University Malaysia, Selangor, Malaysia.

Annals of Operations Research

|June 14, 2021

Summary

This study enhances payment card fraud detection by comparing 13 models on real and public data. Aggregated features, identified via genetic algorithms, significantly improve fraud detection accuracy.

Keywords:

Classification Feature aggregation Fraud detection Payment card Predictive modeling

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Area of Science:

Computer Science
Statistics
Finance

Background:

Increasing payment card usage, particularly online, drives a rise in fraudulent activities.
Payment card fraud poses significant financial risks and losses to the commercial sector annually.
Obtaining real transaction data for fraud detection model development is challenging due to customer confidentiality concerns.

Purpose of the Study:

To evaluate the effectiveness of various statistical and machine learning models for payment card fraud detection.
To compare the performance of original features against aggregated features in identifying fraudulent transactions.
To ascertain if aggregated features identified by genetic algorithms enhance fraud detection discriminative power.

Main Methods:

Application of 13 statistical and machine learning models to analyze payment card transaction data.
Utilizing both publicly available and real transaction datasets for model training and validation.
Employing a genetic algorithm to identify and generate aggregated features.
Conducting a statistical hypothesis test to compare feature sets' discriminative power.

Main Results:

Analysis and comparison of model performance using both original and aggregated features.
Demonstration that aggregated features exhibit superior discriminative power compared to original features.
Positive outcomes from statistical hypothesis testing confirming the advantage of aggregated features.

Conclusions:

Aggregated features identified through genetic algorithms are effective for payment card fraud detection.
The proposed approach using aggregated features can be applied to real-world fraud detection scenarios.
This research contributes to developing more robust and accurate fraud detection systems.