AI in credit scoring: A comprehensive review of models and predictive analytics

This review provides a succinct overview of the comprehensive review exploring the integration of Artificial Intelligence (AI) in credit scoring. The analysis delves into diverse AI models and predictive analytics shaping the contemporary landscape of credit assessment. The review begins by examining the historical context of credit scoring and progresses through the transformative impact of AI on traditional credit assessment methodologies. It scrutinizes various AI models employed in credit scoring, ranging from machine learning algorithms to advanced predictive analytics. Emphasis is placed on elucidating the strengths and limitations of each model, considering factors such as interpretability, accuracy, and scalability. The evolution of credit scoring is discussed, emphasizing the transition from rule-based systems to sophisticated AI-driven approaches. The integration of alternative data sources, such as social media and unconventional financial indicators, is explored, showcasing the expanding scope of AI in capturing a more holistic view of an individual's creditworthiness. The Review underscores the significance of predictive analytics in credit scoring, outlining the nuanced techniques used to forecast credit risk. It elucidates the role of explainable AI, addressing the need for transparency in complex credit scoring models, especially in the context of regulatory compliance and consumer trust. Furthermore, the review highlights the real-world implications of AI in credit scoring, discussing its impact on financial inclusion, risk management, and decision-making processes. The ethical considerations and potential biases associated with AI models are explored, shedding light on the importance of fairness and responsible AI practices in the credit industry. In conclusion, this comprehensive review navigates the intricate landscape of AI in credit scoring, offering a holistic understanding of the models and predictive analytics that underpin modern credit assessment. The synthesis of historical perspectives, model intricacies, and real-world implications makes this review an essential resource for practitioners, researchers, and policymakers in the ever-evolving domain of AI-driven credit evaluation.