Kanallar arası dolandırıcılık tespiti için yeni bir örnekleme tekniği ve gradyan artırıcı ağaç tabanlı yaklaşım

The most recent research on hundreds of financial institutions uncovered that only 26% of them have a team assigned to detect cross-channel fraud. Due to the developing technologies, various fraud techniques have emerged and increased in digital environments. Fraud directly affects customer satisfaction. For instance, only in the UK, the total loss of fraud transactions was £1.26 billion in 2020. In this study, we come up with a Gradient Boosting Tree (GBT)-based approach to efficiently detect cross-channel frauds. As a part of our proposed approach, we developed an algorithm able to generate an optimized training set to train the model and overcome imbalanced data problems. This solution made it easier for the model to understand the concept drift, another major problem arising from changing customer behavior. We boost the performance of our GBT model by integrating additional demographic, economic, and behavioral features as a part of feature engineering. Hyperparameter tuning methods find the best parameters for the model. The cross-channel fraud detection performance of the model is evaluated on a real banking dataset which is highly imbalanced in terms of fraud which is another challenge in the fraud detection problem. We use our trained model to score real-time cross-channel transactions by a leading private bank in Turkey. As a result, our approach can catch almost 75% of total fraud loss in a month with a low false-positive rate and acceptable call count.