Predictive analytics for financial inclusion: Using machine learning to improve credit access for under banked populations

Abstract

This paper explores the application of predictive analytics and machine learning techniques to enhance credit assessment and lending practices. By leveraging alternative data sources, such as mobile phone usage, social media activity, and transactional records, machine learning models can provide more accurate credit risk evaluations for individuals with limited traditional financial histories. The study demonstrates the efficacy of these models through empirical analysis, showcasing their potential to reduce default rates while increasing the approval rates for credit applicants. Furthermore, the paper discusses the ethical considerations and potential biases associated with the use of non-traditional data in credit scoring. The findings underscore the transformative impact of machine learning in fostering financial inclusion, offering practical insights for policymakers, financial institutions, and technology developers aiming to bridge the credit gap for under banked communities. This paper delves into the transformative potential of predictive analytics and machine learning in enhancing financial inclusion by improving credit access for under banked populations. Traditional credit scoring methods often fail to accurately assess the creditworthiness of individuals lacking conventional financial histories, thereby excluding a significant portion of the population from financial services. By incorporating alternative data sources such as mobile phone usage, social media interactions, utility payments, and transactional records, machine learning models can offer more comprehensive and precise credit risk evaluations. The research methodology involves developing and testing various machine learning algorithms, including decision trees, random forests, and neural networks, to predict creditworthiness. The models are trained and validated on datasets that include both traditional financial data and alternative data sources. The performance of these models is measured against standard metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic (ROC) curve. Empirical results indicate that models utilizing alternative data significantly outperform traditional credit scoring methods, leading to higher approval rates for credit applicants while maintaining or improving risk management standards. Keywords: Financial, Inclusion, Predictive, Analytics, Machine Learning, Alternative Data.