This article focuses on the key customer attributes influencing personal loan purchases, which is a key area of interest for financial institutions seeking to expand their asset base. This study specifically examined the transition of debt customers to borrowers at Neo Bank, a US bank. This article identifies and quantifies the key determinants that influence customers' personal loan decisions, providing a reference for customer behavior and preferences. In this work, we developed a predictive model that uses customer data to predict the likelihood of indebted customers getting personal loans. The innovation of this model lies in its ability to identify customer groups with higher conversion potential, enabling targeted marketing strategies. The findings highlight the importance of customer analytics in improving loan acquisition rates and highlight the effectiveness of precision marketing. The conclusions drawn from this study are multifaceted, and by identifying the most important customer attributes, this research helps to develop more effective sales strategies and products to meet the needs of high-potential customer groups. The significance of this study is that it highlights the value of data analytics in driving business growth and customer satisfaction.
Xiao et al. (Tue,) studied this question.