Segmentation of Problematic Loan Customers Using The K-Means Clustering Algorithm to Support Strategic Decision-Making (Case Study: Bank Mega Finance Bengkulu)

Willi Novrian; Annisa Afriani; Julia Purnama Sari; Yusran Panca Putra

Authors

Willi Novrian Universitas Bengkulu
Annisa Afriani Universitas Bengkulu
Julia Purnama Sari Program Studi Sistem Informasi, Fakultas Teknik, Universitas Bengkulu
Yusran Panca Putra Program Studi Sistem Informasi, Fakultas Teknik, Universitas Bengkulu

Keywords:

Non-Performing Loan, K-Means Clustering, CRISP-DM, Data Mining, Davies-Bouldin Index (DBI), Customer Segmentation

Abstract

This study analyzes 5,305 records of non-performing loan customers from Bank Mega Finance Bengkulu using the K-Means Clustering algorithm within the CRISP-DM framework. Based on variables such as tenure, outstanding balance, installment amount, and payment delay duration, the analysis identified three customer risk clusters (high, medium, and low) with a Davies-Bouldin Index (DBI) of 0.201, indicating good clustering quality. The segmentation results can help determine collection priorities, loan restructuring, and risk mitigation strategies, demonstrating the effectiveness of data mining in supporting strategic decision-making in banking risk management.

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