AI-Driven Fraud Detection in Finance: A Cloud-Based Java Approach

Le résumé fourni par la source

The immediate impact of total digitization for financial services has been a significant increase in transaction volumes and the associated increase in the risk of fraudulent activity.This paper provides an AI-enabled fraud detection framework that is implemented with Java and hosted in a cloud environment and provides a broadly scalable and efficient solution for real-time security in finance.The applicable computer science/AI frameworks utilize machine learning/natural language process (NLP) algorithms such as decision trees, clustering models, neural networks to automate the analysis of transaction patterns and anomalies and demonstrated very high prediction success rates.The system architecture is microservices based, allowing for modular development, seamless integrations and dynamic scaling.In addition, cloud-native services are used to facilitate ongoing model training, continuous deployment and cost-effective managed computation resources.This paper considers and discusses other important considerations for financial institutions and/or financial security firms such as data privacy, regulation compliance and fault tolerance for fraud detection systems.Our findings indicate that the proposed comprehensive framework can significantly improve detection rates and operation efficiency, providing a favorable model to address fraud in financial practices when using digital technology in an agile and adaptive manner.

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