Orclever Proceedings of Research and Development

Credit Scoring with Machine Learning Supported by E-Commerce Data

Sema Işık Çalışkan

Hepsipay, D Ödeme Elektronik Para ve Ödeme Hizmetleri A.Ş., R&D Center

https://orcid.org/0000-0001-6796-278X

Tuncer Cem Uğurluer

Hepsipay, D Ödeme Elektronik Para ve Ödeme Hizmetleri A.Ş., R&D Center

https://orcid.org/0009-0004-0699-4330

Emre Arıkan

Hepsipay D Ödeme Elektronik Para ve Ödeme Hizmetleri A.Ş., R&D Center

https://orcid.org/0009-0007-1127-6992

Sinan Uzun

Hepsifinans

https://orcid.org/0000-0001-5274-259X

Muhammet Alper Aydın

Hepsifinans

https://orcid.org/0009-0000-1543-9065

Handan Derya Ercan

Bogazici University

https://orcid.org/0000-0003-1193-6560

Yavuz Selim Hindistan

Ozyegin University

https://orcid.org/0000-0001-9031-8167

DOI: https://doi.org/10.56038/oprd.v7i1.714

Keywords: AI-driven credit scoring, Buy Now Pay Later (BNPL), financial risk assessment, alternative data, fintech, credit risk, machine learning

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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

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