Random Forest and Extreme Learning Machine Algorithms for High Accuracy Credit Card Fraud Detection

Abstract

Abstract: The banking sector is facing a huge issue with credit card fraud, and research has shown that machine learning algorithms are a useful tool for identifying fraudulent actions of this kind. In this investigation, we offer a method for detecting fraudulent use of credit cards that makes use of a hybrid of two machine learning algorithms known as Random Forest (RF) and Extreme Learning Machine (ELM). We compiled a dataset using information obtained from a wide variety of sources, and then we preprocessed it to eliminate any inconsistencies and errors. Following this, the RF and ELM algorithms were put into action and trained on the dataset in order to provide forecasts on the occurrence of fraudulent acts. Measures of performance such as determining how accurate the algorithms are are examples. According to the findings of our research, the ELM algorithm is more accurate than the RF algorithm when it comes to the detection of fraudulent credit card activity.