Artificial Intelligence in Fintech: A Conceptual Framework for AI-Driven Fraud Detection and Risk Management

Abstract

Artificial Intelligence (AI) and financial technology (fintech) are making an impactful stride toward revolutionizing financial fraud detection and risk management within the financial sector. Fintech and artificial intelligence (AI) are driving a significant shift in the way financial institutions detect fraud and manage risk. This paper collects from the most recent studies found in Scopus that focus on fraud detection and risk management systems in the fintech sector, from rule-based systems to machine learning (ML), deep learning (DL), ensemble and graph-based systems. The study synthesizes the results of a structured review on peer-reviewed journal and conference papers, highlighting four major technological trends: (1) Supervised learning and ensemble learning for transaction-level fraud classification, (2) Deep learning and graph networks for relational fraud and money-laundering detection, (3) Explainable AI (XAI) for regulatory transparency, and (4) Governance systems for algorithmic bias and systemic risk. The paper presents an integrated conceptual framework with five layers: Data Ingestion, Feature Engineering, Model Inference, Explain ability and Governance, and Feedback-driven Risk Monitoring, based on these strands. The diagram below shows how explain ability and regulatory compliance can be integrated into AI pipelines instead of being added on as an afterthought. Finally, implications for practitioners and regulators and future avenues of empirical study are discussed, such as federated learning with privacy protection and cross-institutional benchmarking. The results add to the ever-expanding interdisciplinary scholarship on fintech, and provide a reference framework for both research and practical risk management.

LDA is a specialized type of artificial intelligence that processes data into a graph or network structure to detect patterns and relationships among entities. LDA is a specialized form of AI designed to analyze data and identify patterns and relationships between entities, which can then be represented in a graph or network format.