Credit Card Fraud Detection in E-Commerce
| Autor: | Smruthi Mukund, Utkarsh Porwal |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Credit card fraud Supervised learning 02 engineering and technology Machine learning computer.software_genre 020204 information systems Metric (mathematics) Outlier 0202 electrical engineering electronic engineering information engineering False positive paradox 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence Precision and recall Cluster analysis business computer |
| Zdroj: | TrustCom/BigDataSE |
| Popis: | Often the challenge associated with tasks like fraud detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent patterns is because fraudsters continue to innovate novel ways to circumvent measures put in place to prevent fraud. Limited data and continuously changing patterns makes learning significantly difficult. We hypothesize that good behavior does not change with time and data points representing good behavior have consistent spatial signature under different groupings. Based on this hypothesis we are proposing an approach that detects fraudulent patterns in large data sets by assigning a consistency score to each data point using an ensemble of clustering methods. Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns. We also argue that area under the ROC curve, although a commonly used metric to evaluate outlier detection methods is not the right metric. Since outlier detection problems have a skewed distribution of classes, precision-recall curves are better suited because precision compares false positives to true positives (outliers) rather than true negatives (inliers) and therefore is not affected by the problem of class imbalance. The proposed approach is tested on a large real world credit card fraud detection dataset available through Kaggle. We report our performance on both AUPRC and AUROC and show meaningful improvements over the baseline methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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