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

Dalal S., Seth B., Radulesku M., Secara C., Tolea C.

Mathematics, vol.10, no.24, 2022 (SCI-Expanded, Scopus) identifier

  • Nəşrin Növü: Article / Article
  • Cild: 10 Say: 24
  • Nəşr tarixi: 2022
  • Doi nömrəsi: 10.3390/math10244679
  • jurnalın adı: Mathematics
  • Jurnalın baxıldığı indekslər: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Metadex, zbMATH, Directory of Open Access Journals, Civil Engineering Abstracts
  • Açar sözlər: accuracy, financial payment fraud, fraud detection, hyper-parameter tuning, machine learning, nature-inspired optimization, XGBoost
  • Açıq Arxiv Kolleksiyası: Məqalə
  • Adres: Bəli

Qısa məlumat

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.