Cloud-Based AI Models for Credit Risk Assessment: A Scalable and Adaptive Approach

Abstract

Credit risk assessment is a critical component of the financial industry, influencing lending decisions, interest rates, and overall financial stability. Traditional methods of credit risk assessment often rely on static models and manual processes, which can be time-consuming, error-prone, and less accurate. The advent of cloud computing and artificial intelligence (AI) offers a transformative opportunity to enhance the accuracy, scalability, and adaptability of credit risk assessment models. This paper explores the development and deployment of cloud-based AI models for credit risk assessment, focusing on their ability to handle large datasets, learn from new data, and adapt to changing market conditions. We present a comprehensive framework for building, training, and deploying these models, supported by empirical evidence from a case study. The paper also discusses the challenges and potential solutions in implementing cloud-based AI models, including data privacy, model interpretability, and regulatory compliance