LEE- Büyük Veri ve İş Analitiği Lisansüstü Programı

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http://hdl.handle.net/11527/25678

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Konu "consumers" ile LEE- Büyük Veri ve İş Analitiği Lisansüstü Programı'a göz atma
  • Öge
    Overcoming payment behavior challenges: Classifying buy now pay later users with machine learning
    (Graduate School, 2024-08-08) Özdoğan, Ömür ; Ergün, Mehmet Ali ; 528211073 ; Big Data and Business Analytics
    When the economies of developing countries are examined closely, issues such as high debt rates, limited access to funding and difficulties in accessing financing stand out. While Turkey is considered in the category of developing countries, various steps have been taken to strengthen financial stability within the scope of financial tightening policies in recent periods. These steps include steps such as increasing the risk weights of consumer loans, individual credit cards and vehicle loans, and reducing or eliminating the installments on credit cards in certain sectors. Thus, consumers' financial access continues to be increasingly restricted. At this point, Fintech companies offer different alternatives as another option to the restrictions in the traditional financial system. Fintech companies aim to offer low cost, fast and innovative solutions to their customers by using modern data analysis techniques and new generation technologies such as AI. Additionally, these companies aim to reach people who have limited access or are looking for alternatives, in addition to users who do not have access to the banking sector. In this study, first, the development and future of Fintech companies are mentioned and the main product of the study, Buy Now Pay Later, is explained in detail and examples from global BNPL providers are given. Then, credit risk and machine learning techniques in credit risk were also mentioned. During the study, data analysis was performed using sample data of approximately 35,000 customers using the BNPL loan product developed by a London-based Fintech company for one of Turkey's leading retail market chains. A multi-class segmentation problem was designed for users, and popular machine learning methods were used to predict in 3 different classes whether the loans will be paid on time or not. During the experimental phase, Random Forest, Extreme Gradient Boosting and Deep Learning algorithms were tested and performance tables for the models were prepared. When comparing model performances, values ​​such as accuracy, f1-score, roc curve and confusion matrix were determined as priority metrics. As a result, it has been determined that the use of machine learning modeling is quite advantageous when classifying credit risk. However, before deciding on model selection, companies' strategies and policies should always be taken into consideration, and at the same time, the bad loan risks they want to take should be evaluated.