Credit Card Fraud Identification Using Calibrated K nearest Neighbor

Abstract

Credit card fraud has become a significant concern in today`s digital world, leading to substantial financial losses for individuals and businesses alike. Detecting fraudulent transactions accurately and efficiently is crucial for maintaining the security of financial systems. The proposed method combines the power of KNN, a popular classification algorithm, with calibration techniques to enhance the fraud identification performance. Calibration is employed to adjust the probabilities assigned by the KNN algorithm, allowing for more accurate classification decisions and better control over the false positive rate. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a benchmark credit card fraud dataset. The results demonstrate that the calibrated KNN method outperforms the traditional KNN classifier in terms of both accuracy and other performance parameters. The calibrated KNN approach achieves higher fraud detection rates and produces well-calibrated probability estimates, reducing the risk of false alarms or missed fraud cases. This research contributes to the advancement of credit card fraud detection systems and provides valuable insights for financial institutions and individuals concerned with safeguarding against fraudulent activities.