Federated learning for customer digital on-boarding

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Marmara Üniversitesi, Fen Bilimleri Enstitüsü, Türkiye

Tezin Onay Tarihi: 2024

Tezin Dili: İngilizce

Öğrenci: SÜMEYRA TERZİOĞLU

Danışman: Ali Fuat Alkaya

Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu

Özet:

Technological advancements and the pandemic have made digital processes crucial. During the pandemic, most sectors have experienced rapid digitalization. Also, banking regulations have facilitated digital account opening, called digital on-boarding. Formally, digital on-boarding refers to the process of becoming a customer that enables customers to open a bank account remotely and digitally. In the field of competitive finance market, financial institutions need to optimize digital on-boarding process in a sufficiently feasible way. While improving the customer experience, they also need to utilize this first moment of contact with the customer most efficiently. In this process, banks use artificial intelligence for many optimization problems such as customer recognition, cross sell & up-sell, and customer agent matching. Federated learning is a machine learning model that enables learning only through feedback mechanisms without sharing data. Federated learning is preferred in areas where data privacy and protection is of vital importance. In this study, a federated learning model is established using a bank dataset. In the computational experiments, performance of central, local, federated and split neural networks are assessed. Results show that federated learning is statistically better than others and can be exploited in financial services by bringing a remarkable advantage to financial institutions of all sizes, but especially to the smaller ones.