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Credit Scoring with Machine Learning Supported by E-Commerce Data

Sema Işık Çalışkan1,
Tuncer Cem Uğurluer2,
Emre Arıkan3,
Sinan Uzun4,
Muhammet Alper Aydın5,
Handan Derya Ercan6,
Yavuz Selim Hindistan7
1Hepsipay, D Ödeme Elektronik Para ve Ödeme Hizmetleri A.Ş., R&D Center
2Hepsipay, D Ödeme Elektronik Para ve Ödeme Hizmetleri A.Ş., R&D Center
3Hepsipay D Ödeme Elektronik Para ve Ödeme Hizmetleri A.Ş., R&D Center
4Hepsifinans
5Hepsifinans
6Bogazici University
7Ozyegin University
Published:December 31, 2025
DOI: 10.56038/oprd.v7i1.714
Vol. 7, No. 1 · pp. 105–116

Abstract

With the rapid growth of e-commerce, the need for credit in e-commerce has increased. E-commerce platforms require high performance as a competitive advantage in their activities.  Traditional credit risk models need improvement to sustain the performance expected by e-commerce platforms. In this study, we investigate alternative behavioral and transactional variables obtained from an e-commerce platform. We examine whether these variables improve the predictive performance of credit risk models beyond traditional financial data. Our research is based on a real e-commerce environment where a machine learning based credit scoring system was implemented. The study focuses on developing and evaluating a credit risk system that integrates platform specific behavioral data, such as shopping frequency, payment methods, Buy Now Pay Later (BNPL) repayment behavior, and wallet usage, with traditional financial and Credit Bureau(CB) indicators. Our findings demonstrate a significant improvement in model discrimination and Gini performance. The localized AI-driven credit scoring system achieved a low-cost, fast, and more accurate credit assessment.Order

Keywords
AI-driven credit scoringBuy Now Pay Later (BNPL)financial risk assessmentalternative datafintechcredit riskmachine learning

References

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Cite This Article
Çalışkan, S. I., Uğurluer, T. C., Arıkan, E., Uzun, S., Aydın, M. A., Ercan, H. D., Hindistan, Y. S. (2025). Credit Scoring with Machine Learning Supported by E-Commerce Data. *Orclever Proceedings of Research and Development*, 7(1), 105-116. https://doi.org/10.56038/oprd.v7i1.714

Bibliographic Info

JournalOrclever Proceedings of Research and Development
Volume7
Issue1
Pages105–116
PublishedDecember 31, 2025
eISSN2980-020X