The most recent research on hundreds of financial institutions uncovered that only $$26\%$$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 paper, we come up with a Gradient Boosting Tree (GBT)-based approach to efficiently detect cross-channel frauds. As part of our proposed approach, we also figured out a solution to generate training sets from imbalanced data, which also suffers from concept drift problems due to changing customer behaviors. We boost the performance of our GBT model by integrating additional demographic, economic, and behavioral features as a part of feature engineering. We evaluate the performance of our cross-channel fraud detection method on a real banking dataset which is highly imbalanced in terms of frauds 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 $$75\%$$75% of total fraud loss in a month with a low false-positive rate.