Debtor Eligibility Prediction Using Deep Learning with Chatbot-Based Testing

Noviania, Reski and Sela, Enny Itje and Latumakulita, Luther Alexander and Sentinuwo, Steven R. (2024) Debtor Eligibility Prediction Using Deep Learning with Chatbot-Based Testing. Knowledge Engineering and Data Science, 7 (2). pp. 128-138. ISSN 2597-4637

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Abstract

Predicting debtor eligibility is essential for effective risk management and minimizing bad credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy and integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non-experts to interact seamlessly with the system. For financial institutions, this chatbot-based testing approach offers practical benefits by streamlining debtor evaluation processes, reducing dependency on manual assessments, and providing consistent, scalable, and efficient solutions for credit risk management. It allows institutions to handle inquiries outside business hours, ensuring a continuous service flow. Furthermore, the system’s flexibility supports better customer interaction, increasing trust and transparency. By combining advanced machine learning with accessible interfaces, this study offers a scalable solution to improve the precision and practicality of debtor eligibility assessments, making it a valuable tool for modern financial institutions.

Item Type:	Artikel Umum
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	M.Eng. Alfian Ma'arif
Date Deposited:	21 Apr 2025 05:37
Last Modified:	21 Apr 2025 05:37
URI:	http://eprints.uad.ac.id/id/eprint/83109

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