Start Date
16-8-2018 12:00 AM
Description
Fraud related studies cover a wide range of approaches for detection and identifying or mitigating risks associated with it. This study extends the existing body of research and our understanding on fraud with a specific focus on transit media. A large public transit transaction dataset was extracted from production systems of a large U.S Public Transit Authority system. We developed various machine learning models using techniques such as random forests, support vector machines, and artificial neural networks to effectively classify transit fare media fraud into binary groups and compare their relative model performance. We found that random forests and neural networks outperformed support vector machines for transit fraud detection. More data and parameter optimization are needed for improved results.
Recommended Citation
Claiborne, Jay and Gupta, Ashish, "Machine Learning Classifiers for Predicting Transit Fraud" (2018). AMCIS 2018 Proceedings. 37.
https://aisel.aisnet.org/amcis2018/DataScience/Presentations/37
Machine Learning Classifiers for Predicting Transit Fraud
Fraud related studies cover a wide range of approaches for detection and identifying or mitigating risks associated with it. This study extends the existing body of research and our understanding on fraud with a specific focus on transit media. A large public transit transaction dataset was extracted from production systems of a large U.S Public Transit Authority system. We developed various machine learning models using techniques such as random forests, support vector machines, and artificial neural networks to effectively classify transit fare media fraud into binary groups and compare their relative model performance. We found that random forests and neural networks outperformed support vector machines for transit fraud detection. More data and parameter optimization are needed for improved results.