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Performance analysis of metaheuristics based hyperparameters optimization for fraud transactions detection

Abstract

In recent years, detecting fraud transactions has become a popular research topic because credit card fraud transactions result in the loss of billions of dollars every year. Therefore, the need for financial institutions and banks to improve their fraud detection systems is increasing. Financial institutions are increasingly using data mining to develop fraud detection systems that can detect and stop fraudulent transactions automatically. From the standpoint of machine learning, detecting fraud transactions is a binary classification problem. However, interpretability is essential for management to have faith in the used model and to develop fraud prevention strategies. Designing an algorithm that can detect fraud transactions is difficult and needs a higher understanding of each part of the process and a lot of time. However, hyperparameters optimization using metaheuristics techniques reduces the understanding and time needed to handle this issue. Hyperparameters optimization is a technique that is used to select the best hyperparameters that yield the highest performance. Using a metaheuristic approach has many advantages, such as improving the performance of the machine learning model,Tayebi e facilitating the usage of the machine learning model, etc. Our proposed solution in this work is to use metaheuristic algorithms such as genetic algorithms (GA), differential evolution (DE), artificial bee colony algorithm (ABC), grey wolf optimizer algorithm (GWO), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), to optimize hyperparameters and compare these algorithms with grid search method (GS). The used machine learning models in this study are AdaBoost (AD), random forest (RF), logistic regression (LR), support vector machine classifier (SVM), k-nearest neighbors (KNN), mlpclassier (MLP), and decision tree (DT). To compare these optimizers, we use the following evaluation metrics; accuracy, recall, f1-score, precision, and the area under the roc curve (AUC).

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Correspondence to Said El Kafhali.

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Tayebi, M., El Kafhali, S. Performance analysis of metaheuristics based hyperparameters optimization for fraud transactions detection. Evol. Intel. (2022). https://doi.org/10.1007/s12065-022-00764-5

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  • DOI: https://doi.org/10.1007/s12065-022-00764-5

Keywords

  • Machine learning
  • Fraud detection
  • Credit card
  • Hyperparameter optimization
  • Metaheuristic algorithms