Credit card fraud detection: personalized or aggregated model

Mohammed Ibrahim Alowais, Lay Ki Soon

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2012 3rd FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing, MUSIC 2012
Number of pages6
Publication statusPublished - 2012
Externally publishedYes
EventFTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing 2012 - Vancouver, Canada
Duration: 26 Jun 201228 Jun 2012
Conference number: 3rd (Proceedings)


ConferenceFTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing 2012
Abbreviated titleMUSIC 2012
Internet address


  • aggregated model
  • credit card fraud detection
  • Naïve Bayes
  • personalized model
  • Random Forest

Cite this