Designing an AI-Based Credit Risk Rating and Data Analysis Application to Improve the Effectiveness of Financing Risk Management at BPRS UII

Rizqi Adhyka Kusumawati; Arief Darmawan

doi:10.55927/jpmb.v5i6.38

Authors

Rizqi Adhyka Kusumawati Universitas Islam Indonesia, Yogyakarta
Arief Darmawan Universitas Islam Indonesia, Yogyakarta

DOI:

https://doi.org/10.55927/jpmb.v5i6.38

Keywords:

Credit Risk Rating, Artificial Intelligence, BPRS, Risk Management, Credit Scoring

Abstract

BPRS UII is a sharia financial institution facing significant financing risks due to its feasibility assessment process that still relies on subjective analyst assessments. This community service activity aims to design an AI-based Credit Risk Rating (CRR) application and develop a Credit Risk Scoring training module to strengthen financing risk management at BPRS UII. A participatory and collaborative approach was implemented through observation, needs identification, application design, training, mentoring, and evaluation. The CRR application was built using the Flutter platform with the Dart programming language, combining two assessment models: a logic gate-based retail segment model (DSR, SLIK, job stability) and a corporate segment model using a 5C principle expert system with score weighting. The activity results include the development of an application prototype, a standardized training module, and increased employee understanding of risk indicators and rating interpretation. This activity supports BPRS UII's digital transformation in financing risk management while aligning with the principles of prudence and transparency in sharia financial institutions.

References

Addy, W. A., Ajayi-Nifise, A. O., Bello, B. G., Tula, S. T., Odeyemi, O., & Falaiye, T. (2024). AI in credit scoring: A comprehensive review of models and predictive analytics. Global Journal of Engineering and Technology Advances, 18(2), 118–129. https://doi.org/10.30574/gjeta.2024.18.2.0029

Basel Committee on Banking Supervision. (2000). Principles for the management of credit risk. Bank for International Settlements. https://www.bis.org/publ/bcbs75.pdf

Gambacorta, L., Huang, Y., Qiu, H., & Wang, J. (2019). How do machine learning and non-traditional data affect credit scoring? BIS Working Papers No. 834. Bank for International Settlements. https://www.bis.org/publ/work834.pdf

Nallakaruppan, M. K., Chaturvedi, H., Grover, V., Balusamy, B., Jaraut, P., Bahadur, J., Meena, V. P., & Hameed, I. A. (2024). Credit risk assessment and financial decision support using explainable artificial intelligence. Risks, 12(10), 164. https://doi.org/10.3390/risks12100164

National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1

Otoritas Jasa Keuangan. (2018). Peraturan Otoritas Jasa Keuangan Nomor 23/POJK.03/2018 tentang Penerapan Manajemen Risiko bagi Bank Pembiayaan Rakyat Syariah. OJK. https://ojk.go.id/id/regulasi/Pages/Penerapan-Manajemen-Risiko-bagi-Bank-Pembiayaan-Rakyat-Syariah.aspx

Otoritas Jasa Keuangan. (2022). Surat Edaran Otoritas Jasa Keuangan Nomor 11/SEOJK.03/2022 tentang Penilaian Tingkat Kesehatan BPR dan BPRS. OJK. https://ojk.go.id/id/regulasi/Pages/Penilaian-Tingkat-Kesehatan-BPR-dan-BPRS.aspx

Otoritas Jasa Keuangan. (2024). Peraturan Otoritas Jasa Keuangan Nomor 7 Tahun 2024 tentang Bank Perekonomian Rakyat dan Bank Perekonomian Rakyat Syariah. OJK. https://ojk.go.id/id/regulasi/Pages/POJK-7-Tahun-2024-Bank-Perekonomian-Rakyat-dan-Bank-Perekonomian-Rakyat-Syariah.aspx

Otoritas Jasa Keuangan. (2025). Statistik Perbankan Syariah Maret 2025. OJK. https://www.ojk.go.id/id/kanal/syariah/data-dan-statistik/statistik-perbankan-syariah/

Rafi, M. A. (2024). Explainable AI for credit risk assessment: A data-driven approach. Journal of Economics, Finance and Accounting Studies. https://alkindipublishers.org/index.php/jefas/article/view/10157

Shreya, & Pathak, H. (2025). Explainable artificial intelligence credit risk assessment using machine learning. arXiv. https://arxiv.org/abs/2506.19383

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003

World Bank. (2019). Credit scoring approaches guidelines. World Bank Group. https://documents.worldbank.org/