Artificial Intelligence in E-commerce and Banking: Enhancing Customer Experience and Fraud Prevention

Abstract

This has made fraud detection in e-commerce and banking a challenging problem with growing complexity of cyber threats. For instance, machine learning (ML) provides a robust solution by providing for real time an abnormality detect, predictive analyse, and adaptive fraud prevention. The topic of this paper entails an investigation of combining supervised and unsupervised ML models like decision tree, random forest, deep neural network and an auto encoder to accurately identify fraudulent transactions. ML helps fraud prevention mechanisms with the use of behavioral analytics and biometric authentication while reducing the number of false positives. Data sharing in form of federated learning across financial institutions is also handled with the aid of the AI, without risking the user’s privacy. The results indicated that real time fraud detection, adaptive models and complex of blockchain-AI synergy are able to minimize risks. The primary benefit of fraud detection enabled by ML is improving the security of the service but it also comes with the added benefit of inflating customer trust and regulatory compliance. Explainable AI (XAI) advancements in the future will yield additional degree of transparency and reliability in fraud detection models.