Credit Card Fraud Detection: Personalized or Aggregated Model

Banking industry suffers lost in millions of dollars each year caused by credit card fraud. Tremendous effort, time and money have been spent to detect fraud where there are studies done on creating personalized model for each credit card holder to identify fraud. These studies claimed that each card holder carries different spending behavior which necessitates personalized model. However, to the best of our knowledge, there has not been any study conducted to verify this hypothesis. Hence, in this paper, we investigate the effectiveness of personalized models compared to the aggregated models in identify fraud for different individuals. For this purpose, we have collected some actual transactions and some other data through an online questionnaire. We have then constructed personalized and aggregated models. The performance of these models is evaluated using test data set to compare their accuracy in identifying fraud for different individuals. To our surprise, the experimental results show that aggregated models outperforms personalized models. Besides, we have also compared the performance of the random forest and Naïve Bayes in creating the models for fraud detection. Generally, random forest performs better than the Naïve Bayes for the aggregated model while Naïve Bayes performs better in the personalized models.