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Journal : Indonesian Journal of Information System

Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models Linda Wahyu Widianti; Adhitio Satyo Bayangkari Karno; Hastomo, Widi; Aryo Nur Utomo; Dodi Arif; Indra Sari Kusuma Wardhana; Deon Strydom
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10364

Abstract

The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM). According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.
Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models Linda Wahyu Widianti; Adhitio Satyo Bayangkari Karno; Hastomo, Widi; Aryo Nur Utomo; Dodi Arif; Indra Sari Kusuma Wardhana; Deon Strydom
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10364

Abstract

The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM). According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.
Co-Authors Adhitio Satyo Agita Tunjungsari Ahmad Eko Saputro Ahmad Eko Saputro Ahmad Eko Saputro Aji Digdoyo Aji Digdoyo Al-Ghifari, Muhammad Ridho Ambardi Ambardi Ambardi Ambardi Ambardi, Ambardi Amellya, Renny Dwi Aminudin Ardana, Nandika Bayu Arif, Dody Arliando, Tommy Aryo Nur Utomo Asy-Syifa, Zahwa Zia Azie, Yusril Azis, Nur Bakti, Indra Basri, Lody Saladin Bayangkari Karno, Adhitio Satyo Belva, Nasywah Sabina Chufran, Indra Bakti Daruningsih, Kukuh Deon Strydom Deswandi, Arief Diana Yusuf Digdoyo, Aji Dodi Arif Dodi Arif Dody Arif Eka Sally Moreta Eka Sally Moreta Eko Ahmad Eko Ahmad Eko Hadiyanto Elliya Sestri Eva Karla, Eva Fahrul Razi Fahrul Razi Faikoh, Siti Fakhri, Muhamad Naufal Faqihudin Faqihudin Fiedha Nasution Fiqhri, Zul Fitriyani Fitriyani Handayani, Sri Setya Harini Agusta Holmes Rolandy Kapuy Hudaa, Syihaabul Ignatius Joko Dewanto, Ignatius Joko Indra Bakti Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Ire Puspa Wardhani Iwan Setiawan Kalbuana, Nawang Kamilia, Nada Kardian, Aqwam Rosadi Kasoni, Dian Kusuma Wardhana, Indra Sari Linda Wahyu Widianti LM Rasdi Rere LM Rasdi Rere Lussiana ETP Lyscha Novitasari Maeda, Serly Masriyanda, Masriyanda Meika Syahbana Rusli Melyawati Melyawati, Melyawati Merlina, Merlina Muhammad Mardani, Muhammad Nada Kamilia Nada Kamilia Nada Kamilia Nani Kurniawati Natasya, Fatin Nia Yuningsih Nia Yuningsih Nisfiani, Ervina Nur Aini Nuraisyah Nuraisyah Nurhidayati, Aulia Nurmala, Risma Permata, Jelita Prasetyo, Aditya Dwi Purwianti, Zahra Clarita Putra, Yoga Rarasto Putri , Basmallah Ramadhani Aisyah Putri, Dhea Ananda Putri, Syalma Awalya Rahman, Ibadu Rahman, Muhammad Khosyi Rasyiddin, Ahmad Rere, L.M Rasdi Reza Fitriansyah Reza Fitriansyah Rochman, Yuanda Rudy Yulianto Rudy Yulianto Sabillah, Isti’ Anatus Saputro, Ahmad Eko Sestri, Elliya Sestri, Ellya Setiawati, Popong Shevti Arbekti Arman Silvia Ningsih Soegijanto Soegijanto Soleha, Maratus Stevianus Stevianus Sudarto Usuli Sudarwanto, Pantja Sudjiran sudjiran Sukardi, Sukardi Sundoro, Aries Surawan, Tri Sutarno Sutarno Sutarno Sutarno Sutarno Syamsu, Muhajir Syihaabul Hudaa Tri Surawan Tri Surawan Vany Terisia Wardhana , Indra Sari Kusuma Widiyawati, Wita Yayat Sujatna Yayat Sujatna Yayat Sujatna, Yayat Yoga Rarasto Putra Yoga Rarasto Putra Yoga Rarastro Putra Yulianti Muthmainnah, Yulianti Yuningsih, Nia Yusuf Yusuf YUSUF, DIANA Zahratul Azizah