Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)

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Application of Data Mining for Classification of Customer Eligibility at XYZ Bank in Credit Agreements Using Naives Bayes and Random Forest Methods

Authors
Arief Rahman1, *, Ade Sukma Wati1, Aurantia Marina1, Nurul Ilma Hasana Kunio1, Nur Jumrituniisah1
1State Polytechnic of Sriwijaya, Palembang, Indonesia
*Corresponding author. Email: m.arief.rahman@polsri.ac.id
Corresponding Author
Arief Rahman
Available Online 27 February 2024.
DOI
10.2991/978-94-6463-386-3_57How to use a DOI?
Keywords
Data Mining; Naive Bayes; Random Forest; Customer Satisfaction
Abstract

This research addresses the challenges faced by Bank XYZ in assessing loan eligibility through manual analysis of customer data. Utilizing Data Mining techniques, specifically Naive Bayes and Random Forest algorithms, the study aims to enhance the accuracy of classifying customer patterns. The implemented models were evaluated using the CRISP-DM methodology, revealing Naive Bayes with an accuracy of 78.10% and Random Forest with 57.14%. The comparison suggests that Naive Bayes outperforms Random Forest in accuracy. The findings emphasize the potential of Naive Bayes for future implementations in customer classification at XYZ Bank, providing a promising solution to streamline the loan evaluation process and minimize the risk associated with late payments or bad loans.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)
Series
Advances in Engineering Research
Publication Date
27 February 2024
ISBN
10.2991/978-94-6463-386-3_57
ISSN
2352-5401
DOI
10.2991/978-94-6463-386-3_57How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Arief Rahman
AU  - Ade Sukma Wati
AU  - Aurantia Marina
AU  - Nurul Ilma Hasana Kunio
AU  - Nur Jumrituniisah
PY  - 2024
DA  - 2024/02/27
TI  - Application of Data Mining for Classification of Customer Eligibility at XYZ Bank in Credit Agreements Using Naives Bayes and Random Forest Methods
BT  - Proceedings of the 7th FIRST 2023 International Conference on Global Innovations (FIRST-ESCSI 2023)
PB  - Atlantis Press
SP  - 563
EP  - 569
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-386-3_57
DO  - 10.2991/978-94-6463-386-3_57
ID  - Rahman2024
ER  -