Deep Learning for Repayment Prediction in Leasing Companies

Marcin Hernes, Adrianna Kozierkiewicz, Marcin Maleszka, Artur Rot, Agata Kozina, Karolina Matenczuk, Jakub Janus and Ewelina Wrobel

European Research Studies Journal, 2021, vol. XXIV, issue 2 - Part 1, 1134-1148

Abstract: Purpose: This paper aims to improve repayment prediction in leasing companies using a deep learning model. Design/Methodology/Approach: In this work, we prepare some deep learning models and compare them with other solutions based on artificial intelligence like, multiple regression, decision tree, random forest, and bagging classifier. Findings: The developed model enables automatic analysis of large amounts of data that changes quickly and is often unstructured. Additionally, the input vectors consist of specific attributes related to leasing. The results of experiments allow us to conclude that the prediction accuracy of the developed model is higher than reference models used currently in leasing companies. Practical Implications: The developed model has recently been implemented in the Decision Engine system (a system used by leasing companies in Poland) developed by BI Technologies Sp. Z o.o. Company. Originality/Value: Financial institutions automate and simplify credit procedures, eliminating the analyst from the process and replacing him with automatic decision-making processes based on a scoring or similar models. However, to automatically analyze the significance of phenomena occurring in the environment of organizations that affect the assessment of customer's repayments, it is necessary to use artificial intelligence tools.

Keywords: Repayment prediction; deep learning; Fintech; leasing companies; multi-layer neural networks. (search for similar items in EconPapers)
JEL-codes: C45 C61 G23 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ersj.eu/journal/2178/download (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xxiv:y:2021:i:2-part1:p:1134-1148

Access Statistics for this article

More articles in European Research Studies Journal from European Research Studies Journal
Bibliographic data for series maintained by Marios Agiomavritis ().