SYSTEMATIC LITERATURE REVIEW: PERAN KONSEP MATEMATIKA DISKRIT DALAM PENGEMBANGAN MODEL CREDIT SCORING PEMBIAYAAN PADA LEMBAGA KEUANGAN SYARIAH NON-BANK
DOI:
https://doi.org/10.30739/jpsda.v6i2.5286Keywords:
Credit Scoring, Discrete Mathematics, Non-Bank Islamic Financial Institutions, PRISMA, Systematic Literature ReviewAbstract
This study examines the integration of discrete mathematics into financing scoring systems for non-bank Islamic financial institutions (NBIFIs). A Systematic Literature Review was selected to consolidate evidence on mathematical structures, decision-support methods, predictive algorithms, and sharia governance, with reporting guided by PRISMA 2020. Searches in Google Scholar, Scopus, Garuda, Semantic Scholar, and PMC for publications from 2020 to 2026 identified 487 records; 89 remained after deduplication and title-abstract screening, 34 full texts were assessed, and 20 articles were included. Machine-learning and credit-scoring studies dominated the evidence base (35%), followed by studies on NBIFIs and Islamic non-bank finance (25%), PRISMA-based reviews (15%), discrete mathematics and decision modelling (15%), and DSS/MCDM applications (10%). The findings show that finite sets, logical rules, decision trees, graph structures, and ranking matrices are relevant to financing assessment, but explicit discrete-mathematics applications remain rare. Major gaps concern limited NBIFI-specific datasets, weak operationalisation of maqashid al-shariah, insufficient fairness testing, and scarce external validation. The review proposes a four-layer model linking input data, discrete-mathematical structures, hybrid scoring algorithms, and explainable decisions under sharia governance. Practically, NBIFIs should develop auditable scoring rules, involve Sharia Supervisory Boards in model validation, and provide assistance pathways for applicants below the eligibility threshold.
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Abdullah, O., Shaharuddin, A., Wahid, M. A., & Harun, M. S. (2022). The potentials and challenges of decision support system for Islamic banking and finance. European Journal of Islamic Finance, 9(3), 21-29. https://doi.org/10.13135/2421-2172/6901
Ahmad, F., et al. (2022). Studi literature: General issue lembaga keuangan non-bank syariah di Indonesia. El-Mal: Jurnal Kajian Ekonomi dan Bisnis Islam, 3(5). https://doi.org/10.47467/elmal.v3i5.1056
Auda, J. (2008). Maqasid al-Shariah as philosophy of Islamic law: A systems approach. International Institute of Islamic Thought.
Bücker, M., Szepannek, G., Gosiewska, A., & Biecek, P. (2022). Transparency, auditability, and explainability of machine learning models in credit scoring. Journal of the Operational Research Society, 73(1), 70-90. https://doi.org/10.1080/01605682.2021.1922098
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57, 203-216. https://doi.org/10.1007/s10614-020-10042-0
Dastile, X., & Çelik, T. (2021). Making deep learning-based predictions for credit scoring explainable. IEEE Access, 9, 50426-50440. https://doi.org/10.1109/ACCESS.2021.3068854
Dastile, X., Çelik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91, 106263. https://doi.org/10.1016/j.asoc.2020.106263
Fauziah, R., et al. (2025). Systematic literature review dengan metode PRISMA: Dampak literasi keuangan syariah terhadap keputusan menggunakan produk perbankan syariah. JIM: Jurnal Ilmiah Mahasiswa, 7(2). https://doi.org/10.47466/jim.v7i2.13377
Fritz-Morgenthal, S., Hein, B., & Papenbrock, J. (2022). Financial risk management and explainable, trustworthy, responsible AI. Frontiers in Artificial Intelligence, 5, 779799. https://doi.org/10.3389/frai.2022.779799
Gramespacher, T., & Posth, J.-A. (2021). Employing explainable AI to optimize the return target function of a loan portfolio. Frontiers in Artificial Intelligence, 4, 693022. https://doi.org/10.3389/frai.2021.693022
Hadji Misheva, B., Jaggi, D., Posth, J.-A., Gramespacher, T., & Osterrieder, J. (2021). Audience-dependent explanations for AI-based risk management tools: A survey. Frontiers in Artificial Intelligence, 4, 794996. https://doi.org/10.3389/frai.2021.794996
Irwan, M., et al. (2025). Explainable boosting machine for transparent risk assessment in microfinance. ILKOM Jurnal Ilmiah, 17(3), 312-322. https://doi.org/10.33096/ilkom.v17i3.3214.312-322
Iskandar, A., et al. (2024). Decision support system for recommendation sharia banking investment products using Simple Additive Weighting (SAW). bit-Tech: Jurnal Teknologi Informasi, 7(2), 505-514. https://doi.org/10.32877/bt.v7i2.1872
Islam, A. M. S., & Ulinnuha, A. (2024). Perbankan dan industri keuangan nonbank (IKNB) syariah. Bumi Aksara.
Jammalamadaka, K. R., & Itapu, S. (2023). Responsible AI in automated credit scoring systems. AI and Ethics, 3(2), 485-495. https://doi.org/10.1007/s43681-022-00175-3
Kumar, A., Sharma, S., & Mahdavi, M. (2021). Machine learning technologies for digital credit scoring in rural finance: A literature review. Risks, 9(11), 192. https://doi.org/10.3390/risks9110192
Lestari, P., et al. (2024). Pendekatan operasi union sets melalui prinsip inklusi-eksklusi dalam matematika diskrit pada konteks ekonomi Islam. Journal of Islamic Economics and Business Research, 8(2). https://doi.org/10.30737/jiebr.v8i2.5322
Lestari, W., & Pebruary, S. (2025). Analisis determinan kolektibilitas pembiayaan pada KSPPS BMT Amanah Nusa Jepara. Jurnal Tabarru': Islamic Banking and Finance, 8(1), 1-14. https://doi.org/10.25299/jtb.2025.vol8(1).20828
Mukit, M. M. H., Hasan, F., Choudhury, T., Al Fadli, A., & Fadul, A. (2026). Machine learning and artificial intelligence powered credit scoring models for Islamic microfinance institutions: A blockchain approach. Risks, 14(1), 12. https://doi.org/10.3390/risks14010012
Munir, R. (2020). Matematika diskrit (Revisi ke-7). Informatika.
Nallakaruppan, M. K., Balusamy, B., Shri, M. L., Malathi, V., & Bhattacharyya, S. (2024). An explainable AI framework for credit evaluation and analysis. Applied Soft Computing, 153, 111307. https://doi.org/10.1016/j.asoc.2024.111307
Nugraha, A., et al. (2025). A systematic literature review on the relationship between Islamic financial literacy and banking behavior. Kunuz: Journal of Islamic Banking and Finance, 5(2). https://doi.org/10.30984/kunuz.v5i2.1647
Otoritas Jasa Keuangan. (2026). Snapshot perbankan syariah Indonesia: Desember 2025.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hrobjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Putri, N. A., et al. (2024). Tantangan dan peluang pembiayaan syariah non-bank dalam meningkatkan kesejahteraan masyarakat. JURIMEA, 4(2), 50-68. https://doi.org/10.55606/jurimea.v4i2.872
Rahmat, M., et al. (2024). Peran industri keuangan non-bank berbasis syariah dalam mendukung pertumbuhan ekonomi. Jurnal Akuntansi Ekonomi dan Manajemen, 1(4). https://doi.org/10.61722/jaem.v1i4.3319
Sari, M. I., et al. (2025). Analysis bank syariah dan non-bank syariah terhadap pertumbuhan ekonomi. RIGGS, 4(4), 3331-3340. https://doi.org/10.31004/riggs.v4i4.4102
Sari, R., et al. (2024). Advanced credit scoring with Naive Bayes algorithm. E-KOMTEK, 8(2). https://doi.org/10.37339/e-komtek.v8i2.2160
Siregar, K. H., Ruslan, D., Faried, A. I., & Sembiring, R. (2025). Implementation of machine learning algorithm for credit scoring prediction in Islamic microfinance. Journal of Intelligent Systems and Information Technology, 2(2), 88-98. https://doi.org/10.61971/jisit.v2i2.156
Supriadi, I., Maghfiroh, R. U., & Abadi, R. (2025). Implementing innovative credit scoring (ICS) for credit risk assessment and loan origination. The International Journal of Financial Systems, 3(1), 99-112. https://doi.org/10.61459/ijfs.v3i1.36
Supriyanto, A., et al. (2025). Analisis perbandingan machine learning untuk prediksi kelayakan kredit. IT-Explore, 4(1), 82-92. https://doi.org/10.24246/itexplore.v4i1.2025.pp82-92
Suwardi, D., et al. (2026). Integration of shariah audit and shariah governance in supporting ESG compliance: A systematic literature review. Owner: Riset dan Jurnal Akuntansi, 10(2), 972-985. https://doi.org/10.33395/owner.v10i2.3213
Wahyudi, I., et al. (2022). Decision support system for determining customer feasibility to grant credit using comparisons of TOPSIS and SAW method. Jurnal Teknik Informatika, 3(5), 1231-1238. https://doi.org/10.20884/1.jutif.2022.3.5.369
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