Department of Computer Science and Engineering

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Hybrid Approach to Improve the performance of Ensemble Techniques in Financial Fraud Detection
(Department of Computer Science & Engineering, NITS, 2022) Borah, Abhigyan
In recent times, the use of payment mediums like credit cards have increased and thus the number of fraudulent transactions as well. In these situations where anomaly detection is very important, an unbalanced data-set can lead to a drastic fall in the performance of the classification algorithms. If the majority instances outnumber the number of instances of the minority class it is known as Class Imbalance. Methods like ensemble techniques and sampling techniques have been proposed to handle class imbalance. The ensemble techniques with and without sampling have been executed and it was observed that with the traditional ensemble techniques to detect credit card frauds, the performance of some of the algorithms is considerably low as compared to others. Thus, with the aim to improve the performances of those algorithms, a hybrid approach has been proposed, where two of the worst-performing ensemble techniques have been taken based on their F1 scores. This model takes the output of these two ensemble techniques and predicts the final output based on a logical connector. It was observed that in some of the cases, the improvement in the F1 score is as high as 15.36%, whereas in some other cases the improvements are very minute.