Predictive Analytics for Buy Now, Pay Later: Assessing Consumer Impulsivity and Credit Risk for Informed Decision-Making

Technology has opened up new lending opportunities. The Buy Now, Pay Later service has grown in popularity due to its ease of use. In India, millions of customers have registered for BNPL platforms, which provide a variety of liquidity-enhancing services. Shopping, bill paying, and ticket booking are among the services provided with Pay Later alternatives. BNPL data will provide information on customer behaviour in Nagpur. Decision-making inside the BNPL system can provide more reasonable solutions based on these findings, improving credit risk assessment accuracy. A two-step process for assessing credit risk and analyzing the spending habits of denied applicants is described. Credit concentration has characteristics that reduce rejection rates while increasing profitability. Following the dual-approval procedure, credit risk outcomes will be analyzed to reveal previously unknown customer behavior patterns in applicant rejections and to enable predictive segmentation of the remaining platform users. The results of the data analysis software design are clear. This capacity may determine the customer's credit requirements, identify behaviors that cause purchase urgency, and offer the most relevant solutions to the client. High-performing solutions can increase client engagement with their platform while also streamlining the customer journey and mitigating credit risks.