Replication Data for: Alternative Datasets for Credit Scoring of Thin File Consumers

Credit scoring is essential in financial services, allowing institutions to assess consumers' creditworthiness. Traditional credit scoring models heavily rely on extensive transaction history, which often poses a significant challenge for thin-file consumers—individuals with limited credit history. This comprehensive review aims to explore and evaluate various alternative datasets that can be utilised to improve credit scoring for thin-file consumers. By moving beyond traditional transaction profiles, alternative datasets such as social media data, web browsing behaviour, digital footprints, and telecom data offer new dimensions to assess consumer credit risk. Additionally, the review compares the effectiveness of various machine learning algorithms, including support vector machines, neural networks, decision trees, random forests, and hybrid models, in leveraging these datasets for credit scoring. The findings indicate that integrating multiple alternative data sources with advanced machine learning algorithms can significantly improve the accuracy and reliability of credit risk assessments. The comparative analysis of machine learning algorithms used in credit scoring highlights the strengths and limitations of different approaches. Support vector machines (SVM), neural networks, decision trees, random forests, and hybrid models have all shown varying degrees of success in utilising alternative datasets for credit scoring. Hybrid models combine multiple machine learning techniques and are particularly effective in leveraging diverse data sources to provide a robust credit risk assessment. This review underscores the potential of alternative datasets in revolutionizing credit scoring for thin-file consumers. By incorporating new data dimensions and advanced machine learning algorithms, researchers can improve their ability to assess credit risk accurately. Future researchers may continue to refine these models and explore new alternative datasets to enhance credit scoring models using machine learning algorithms.