Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network
Arnold Adimabua Ojugo, Obinna Nwankwo
Abstract
Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.
Topics & Concepts
Credit card fraudCredit cardComputer scienceModular designArtificial neural networkGenetic algorithmModular neural networkArtificial intelligenceFocus (optics)Cluster analysisIntrusion detection systemMachine learningAlgorithmData miningComputer securityTime delay neural networkOperating systemWorld Wide WebPaymentPhysicsOpticsImbalanced Data Classification TechniquesFinancial Distress and Bankruptcy Prediction