Utilizing AI-driven predictive analytics to reduce credit risk and enhance financial inclusion

This review paper explores the transformative impact of AI-driven predictive analytics on reducing credit risk and enhancing financial inclusion. The paper begins with an overview of financial inclusion and traditional credit risk management practices, highlighting their challenges and limitations. It then examines how AI and predictive analytics technologies are revolutionizing these practices by leveraging advanced machine learning algorithms, alternative data sources, and real-time analytics. Key applications discussed include enhancing credit scoring accuracy, real-time risk assessment and monitoring, fraud detection, and developing early warning systems. These applications enable financial institutions to make more informed decisions, extend credit to underserved populations, and tailor personalized financial products. Despite these advancements, the paper also addresses critical challenges such as bias in AI models, data privacy concerns, and regulatory considerations. Ethical implications are explored, emphasizing the importance of fairness, transparency, and accountability in deploying AI-driven financial solutions. Lastly, the paper outlines potential future developments in AI-driven predictive analytics, including advances in real-time decision-making, enhanced financial literacy tools, and collaborative efforts to scale inclusive financial ecosystems globally.