Risk Management in BNPL Systems: Leveraging Machine Learning for Credit Scoring
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I3P106Keywords:
Buy Now Pay Later (BNPL), Credit Scoring, Risk Management, Machine Learning, Financial ServicesAbstract
The BNPL option has brought a big change in the financial sector because of its option of easy payment services. However, BNPL service has expanded its application rapidly, which has brought important risk factors into question especially concerning credit scoring and risk management. The BNPL customer profile deviates from traditional borrowers' behavior, and this is why conventional credit scoring methodologies are often insufficient in capturing the risks related to BNPL customers. In this paper, the purpose is to analyze the implementation of ML methods in BNPL systems to improve credit scoring and risk assessment. To sum up, we give a detailed description of different types of ML algorithms, general and specific to credit scoring, and briefly outline the possible advantages and risks of their application. Therefore, the purpose of our research is to shed light on BNPL services, emphasizing how the use of enriched data analysis can tackle the existing issues and make BNPL services sustainable.
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