Optimizing High-Performance Weighted Extreme Learning Machine with DAPM for Imbalance Credit Card Fraud Detection

Abstract

Fraudulent activities associated with financial transactions are observed in the present scenario, especially with the use of credit cards, at a fast rate. As banking services are rising in digitalization and mobile banking is on the increase in structured written requests, credit card payments rates rise all year, with billions of transactions detected as unfair. For financial institutions to maintain the goodwill of their customers a fraud detection system requiring different detection strategies is therefore extremely important. Researchers and practitioners, using different algorithms, have proposed many methods for fraud detection to find the pattern of fraud. Data mining (DM) algorithms were influential to detect fraudulent transactions by combating fraudsters' attacks on the classic frameworks for preventing fraud. The paper aims to classify fraudulent transactions by Weighted Extreme Learning Machine (WELM) classifiers of 2 Artificial Neural networks (ANN) and 3 separate data sets of Credit Card Fraud (CCF). We use a high-performance Weighted Extreme Learning Machine (HPWELM). The efficiency of the classifiers is calculated based on accuracy, precision, recall & G-mean. The research work has been implemented in Python 3.6. The results are represented in form of tables & snapshots. Results demonstrate that accuracy of the HPWELM classifier has achieved a remarkable improvement in the training and testing phases of the algorithm.