Big Data and Artificial Intelligence for Fintech Innovation Systematic Literature Review

Abstract

Big Data Analytics (BDA) and Artificial Intelligence (AI) have emerged as key drivers of fintech innovation, enabling financial institutions to improve operational efficiency, risk management, customer services, and data-driven decision making. However, the growing literature remains fragmented, highlighting the need for a comprehensive synthesis of current knowledge. Objective This study aims to systematically review the literature on the integration of BDA and AI in fintech innovation, focusing on major research themes, applications, challenges, and future opportunities. Method A Systematic Literature Review (SLR) was conducted using the PRISMA framework. Relevant studies published between 2020 and 2025 were selected from major academic databases based on pre- defined criteria and analyzed using qualitative thematic analysis. Results The findings indicate that BDA and AI enhance fintech innovation through fraud detection, credit risk assessment, customer behavior prediction, personalized financial services, roboadvisory, and RegTech. Key challenges include data privacy, cybersecurity risks, algorithmic bias, limited AI explainability, regulatory uncertainty, and legacy system integration. Emerging technologies, including Explainable AI (XAI), federated learning, and generative AI, offer opportunities to improve transparency, trust, and scalability. Conclusion The integration of BDA and AI plays a strategic role in accelerating fintech innovation, supporting more intelligent, efficient, secure, and customer centric financial services. This review synthesizes recent research, identifies key research gaps, and provides insights for future research and managerial decision-making to support sustainable fintech innovation