The Critical Role of Alternative Datasets in Credit Assessment Using Machine Learning Techniques

This research investigates the growing significance of alternative datasets in credit assessment using machine learning (ML) techniques, with a focus on addressing the limitations of traditional credit scoring methods. Conventional models rely heavily on historical financial data, often excluding thin-file consumers—individuals with limited or no credit history—from financial systems. By incorporating alternative data sources, such as digital footprints, psychometric data, and social media activity, credit scoring models can provide a more comprehensive evaluation of creditworthiness. The study explores the potential of ML models, particularly ensemble classifiers and deep learning techniques, to enhance predictive accuracy. Compared to traditional statistical methods like logistic regression, advanced ML models show superior performance in handling complex, unstructured data and improving inclusivity in credit assessment. Additionally, deep learning techniques such as Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) can detect complex patterns within alternative datasets, further enhancing model precision. The paper emphasizes the need for developing explainable AI models and exploring hybrid approaches that integrate supervised and unsupervised learning techniques. These future research directions will enable more robust credit scoring models that can cater to thin-file consumers and promote financial inclusion, thus enhancing the overall effectiveness of credit assessment.