(untitled) (Internet of Things and Edge Computing Journal, 2023)
In the ever-evolving landscape of banking and insurance, the accurate assessment of credit risk stands as a cornerstone for financial stability and profitability. With the advent of machine learning techniques, the methodologies for credit risk assessment have witnessed a transformative shift, promising enhanced predictive capabilities and risk segmentation. This paper delves into the comprehensive investigation of machine learning approaches tailored for credit risk assessment within the banking and insurance sectors, with a focal point on predictive modeling for loan default prediction, credit scoring, and risk segmentation. The journey begins with an exploration of the fundamental concepts underlying credit risk assessment, shedding light on its pivotal role in ensuring prudent lending practices and mitigating potential financial losses. Following this, a detailed overview of traditional credit risk assessment methods is presented, highlighting their limitations in coping with the complexities of modern financial landscapes characterized by vast datasets and dynamic risk profiles. The subsequent sections delve into the realm of machine learning, elucidating its principles and methodologies pertinent to credit risk assessment. Various machine learning algorithms, including but not limited to logistic regression, decision trees, random forests, support vector machines, and neural networks, are dissected in terms of their applicability and efficacy in predicting loan defaults, generating credit scores, and delineating risk segments. Furthermore, the paper explores the challenges and considerations inherent in the application of machine learning techniques to credit risk assessment, encompassing issues such as data quality, model interpretability, regulatory compliance, and ethical considerations. Strategies for data preprocessing, feature selection, model evaluation, and validation are elucidated to ensure robust and reliable credit risk models. Drawing upon empirical studies and case examples from the banking and insurance sectors, the efficacy and real-world applicability of machine learning approaches for credit risk assessment are demonstrated. Insights gleaned from these empirical analyses serve to underscore the potential of machine learning in enhancing the accuracy, efficiency, and agility of credit risk management processes. In conclusion, this paper encapsulates a holistic examination of machine learning approaches for credit risk assessment in banking and insurance, delineating their transformative potential in augmenting predictive modeling for loan default prediction, credit scoring, and risk segmentation. By embracing machine learning techniques, financial institutions can fortify their risk management frameworks, foster sound lending practices, and navigate the intricate landscapes of modern finance with confidence and resilience.