Predicting Fraud in Financial Payment Services through Optimized Hyper-Parameter-Tuned XGBoost Model

Surjeet Dalal, Bijeta Seth, Magdalena Radulescu (), Carmen Secara and Claudia Tolea
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Surjeet Dalal: Department of Computer Science and Engineering, Amity University Haryana, Gurugram 122413, Haryana, India
Bijeta Seth: Department of Computer Science and Engineering, B. M. Institute of Engineering & Technology, Sonipat 131001, Haryana, India
Magdalena Radulescu: Department of Finance, Accounting and Economics, University of Pitesti, 110001 Pitesti, Arges, Romania
Carmen Secara: Department of Management and Business Administration, University of Pitesti, 110001 Pitesti, Arges, Romania
Claudia Tolea: Department of Management and Business Administration, University of Pitesti, 110001 Pitesti, Arges, Romania

Mathematics, 2022, vol. 10, issue 24, 1-17

Abstract: Online transactions, medical services, financial transactions, and banking all have their share of fraudulent activity. The annual revenue generated by fraud exceeds $1 trillion. Even while fraud is dangerous for organizations, it may be uncovered with the help of intelligent solutions such as rules engines and machine learning. In this research, we introduce a unique hybrid technique for identifying financial payment fraud by combining nature-inspired-based Hyperparameter tuning with several supervised classifier models, as implemented in a modified version of the XGBoost Algorithm. At the outset, we split out a sample of the full financial payment dataset to use as a test set. We use 70% of the data for training and 30% for testing. Records that are known to be illegitimate or fraudulent are predicted, while those that raise suspicion are further investigated using a number of machine learning algorithms. The models are trained and validated using the 10-fold cross-validation technique. Several tests using a dataset of actual financial transactions are used to demonstrate the effectiveness of the proposed approach.

Keywords: financial payment fraud; fraud detection; machine learning; XGBoost; nature-inspired optimization; hyper-parameter tuning; accuracy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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