Predictive risk assessment credit scoring using supervised learning - Khor, Wei Heng (2025)
This study explores the application of supervised learning models within credit scoring, aiming to revolutionize risk assessment in lending decisions. The primary goal involves comparing these advanced methodologies against conventional credit assessment techniques to ascertain their effectiveness in determining creditworthiness. In response to the escalating complexity of financial transactions and the wealth of available data, this research seeks to elevate the precision and efficiency of credit risk evaluation. Supervised learning, known for its ability to learn from labelled datasets, presents an opportunity to redefine credit scoring by leveraging historical credit information. The core focus is on assessing the predictive capabilities of supervised learning algorithms—specifically Logistic Regression, Random Forest, K-Nearest Neighbours, Support Vector Machines and Gradient Boosting—against established credit scoring methods. By harnessing the power of these modern techniques and analysing intricate credit patterns, this research endeavours to deliver more accurate credit risk assessments. It strives to surpass the existing industry norms by using machine learning models to refine credit evaluation processes.