Cross-Organizational Transferability of Early Default Prediction Models in Indonesia’s Motorcycle Leasing Industry

Authors

  • Ivan Kristiawan 1CV Mitra Sistem Solusi, Yogyakarta, Indonesia, 55161
  • Twin Yoshua R. Destyanto Department of Industrial Engineering, Universitas Atma Jaya Yogyakarta
  • Jeremy Laurence Bañez Industrial Engineering Department, Bulacan State University, City of Malolos, Bulacan, Philippines, 3000

DOI:

https://doi.org/10.24002/ijis.v8i2.13954

Abstract

The motorcycle-leasing sector in Indonesia is critical for consumer financing, yet firms face persistent difficulty in predicting early installment defaults because most credit-risk models are built for single organizations, forcing companies to repeatedly rebuild models when policies or operations change, which increases costs and delays risk detection. This study examines whether early default prediction models developed in one motorcycle-leasing company can be transferred to others by applying a hierarchical framework that integrates feature engineering, behavioral clustering, and supervised classification. The model was trained on 113,222 fiduciary contracts (2011–2025) from a Batam-based firm and tested on two external firms in Batam and Jakarta using Logistic Regression, Random Forest, and LightGBM. Results show substantial performance decline for the second Batam firm but relatively stable performance in Jakarta, indicating that organizational policy differences matter more than regional factors. Fine-tuning with sufficient local data improves performance, while limited data creates instability. The study provides a practical foundation for scalable and transferable credit-risk modeling in emerging markets.

Keywords: credit risk prediction; transfer learning; domain adaptation; motorcycle leasing; clustering.

References

[1] P. Prameswari and T. F. Alfiany, “Juridical Analysis of Over Credit in Motor Vehicle Credit Agreement with Fiduciary Guarantee in View of Undang-Undang Nomor 42 of 1999 on Fiduciary Guarantee,” Journal of Law, Politic and Humanities, vol. 4, no. 6, pp. 2390–2401, Oct. 2024, doi: 10.38035/JLPH.V4I6.700.

[2] S. J. Lesmana and R. Ristiyana, “Legal Counseling Regarding the Limits of Debt Collector and Leasing Authority in the Execution of Motor Vehicles,” Help: Journal of Community Service, vol. 1, no. 4, pp. 246–255, Mar. 2025, doi: 10.62569/HJCS.V1I4.135.

[3] C. Tuccillo, “Termination for non-performance in transactional leasing: the latest interventions, fair compensation, blockchain and residential real estate leasing. Reflections from Cass., 28 January 2021, no. 2061 and Cass., 14 October 2021, no. 28023,” European Journal of Privacy Law and Technologies, vol. 2023, no. 1, pp. 174–207, 2023, doi: 10.57230/EJPLT223CT.

[4] H. Adjie and E. Sinaga, “Implementation of Motorcycle Rental Agreement,” Budapest International Research and Critics Institute-Journal (BIRCI-Journal), vol. 4, no. 4, pp. 9353–9362, Nov. 2021, doi: 10.33258/BIRCI.V4I4.2947.

[5] N. F. Hidayanti, A. Hulaimi, and Z. Ariani, “Vehicle Trade of Defaulted Finance Lease Companies in Mataram City Sharia Economic Perspective,” Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE), vol. 7, no. 3, pp. 7882–7896, Dec. 2024, doi: 10.31538/IIJSE.V7I3.5693.

[6] R. Fujinuma and Y. Asahi, “Default Factors in Motorcycle Sales in Developing Countries,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13305 LNCS, pp. 324–336, 2022, doi: 10.1007/978-3-031-06424-1_24.

[7] O. Triki and F. Abid, “Convertible lease risk spread modeling with correlation,” Decisions in Economics and Finance 2024 48:1, vol. 48, no. 1, pp. 747–773, Oct. 2024, doi: 10.1007/S10203-024-00490-W.

[8] S. J. Francis, H. G. C. Hansamali, M. Ganeshamoorthy, and R. Abarna, “Exploring Credit Risk Management and Financial Performance: Evidence from People’s Leasing and Finance PLC,” Wayamba Journal of Management, vol. 15, no. 2, pp. 86–100, May 2025, doi: 10.4038/WJM.V15I2.7630.

[9] Y. C. Chang, K. H. Chang, and W. T. Chen, “Vehicle leasing credit risk assessment modeling by applying extended logistic regression,” Journal of Intelligent and Fuzzy Systems, vol. 45, no. 4, pp. 5211–5222, Oct. 2023, doi: 10.3233/JIFS-231344.

[10] H. Li et al., “A novel method for credit scoring based on feature transformation and ensemble model,” PeerJ Comput Sci, vol. 7, p. e579, 2021, doi: 10.7717/PEERJ-CS.579.

[11] L. O. Hjelkrem and P. E. de Lange, “Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data,” Journal of Risk and Financial Management 2023, Vol. 16, Page 221, vol. 16, no. 4, p. 221, Apr. 2023, doi: 10.3390/JRFM16040221.

[12] V. Chang, S. Sivakulasingam, H. Wang, S. T. Wong, M. A. Ganatra, and J. Luo, “Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers,” Risks 2024, Vol. 12, Page 174, vol. 12, no. 11, p. 174, Nov. 2024, doi: 10.3390/RISKS12110174.

[13] X. Zhang et al., “Data-Driven Loan Default Prediction: A Machine Learning Approach for Enhancing Business Process Management,” Systems 2025, Vol. 13, Page 581, vol. 13, no. 7, p. 581, Jul. 2025, doi: 10.3390/SYSTEMS13070581.

[14] H. Suryanto, A. Mahidadia, M. Bain, C. Guan, and A. Guan, “Credit Risk Modeling Using Transfer Learning and Domain Adaptation,” Front Artif Intell, vol. 5, p. 868232, May 2022, doi: 10.3389/FRAI.2022.868232/BIBTEX.

[15] F. Xu and R. Zhang, “Explainable Domain Adaptation Learning Framework for Credit Scoring in Internet Finance Through Adversarial Transfer Learning and Ensemble Fusion Model,” Mathematics 2025, Vol. 13, Page 1045, vol. 13, no. 7, p. 1045, Mar. 2025, doi: 10.3390/MATH13071045.

[16] Y. Suh, “Machine learning based customer churn prediction in home appliance rental business,” Journal of Big Data 2023 10:1, vol. 10, no. 1, pp. 41-, Apr. 2023, doi: 10.1186/s40537-023-00721-8.

[17] M. Ay, L. Özbakır, S. Kulluk, B. Gülmez, G. Öztürk, and S. Özer, “FC-Kmeans: Fixed-centered K-means algorithm,” Expert Syst. Appl., vol. 211, p. 118656, Jan. 2023, doi: 10.1016/j.eswa.2022.118656.

[18] A. Kozina et al., “The default of leasing contracts prediction using machine learning,” Procedia Comput. Sci., vol. 225, no. 10, pp. 424–433, Jan. 2023, doi: 10.1016/j.procs.2023.10.027.

[19] D. Ghosh and S. Chatterjee, “Journal of Strategic Marketing Customer segmentation for stigmatized products using point-of-sale big-data and Gaussian-Mixture-Model: an application in female-hygiene market Customer segmentation for stigmatized products using point-of-sale big-data and…,” 2025, doi: 10.1080/0965254X.2025.2562444.

[20] E. Y. Jin, W. Jiang, and Z. Zheng, “A novel method for testing adverse selection with IoT data: Evidence from China’s auto insurance market,” Decis. Support Syst., vol. 200, p. 114576, Jan. 2026, doi: 10.1016/j.dss.2025.114576.

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Published

2026-02-28

How to Cite

Kristiawan, I., Destyanto, T. Y. R., & Bañez, J. L. (2026). Cross-Organizational Transferability of Early Default Prediction Models in Indonesia’s Motorcycle Leasing Industry. Indonesian Journal of Information Systems, 8(2), 130–143. https://doi.org/10.24002/ijis.v8i2.13954

Issue

Vol. 8 No. 2 (2026): February 2026

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Articles

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