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Apr 22, 2025
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Abstract
This study examines the integration of financial mathematics and artificial intelligence (AI), focusing on credit risk analysis within the financial industry. Traditionally, financial mathematics has played a central role in modeling and assessing credit risk. However, recent technological advancements have enabled a more synergistic relationship between financial mathematics and AI, particularly through the use of machine learning. This integration enhances risk assessment by leveraging classification algorithms to uncover complex patterns in historical data patterns that are often difficult to detect using conventional methods. Through a case study in the financial sector, this research explores the application of classification algorithms to better understand customer behavior, evaluate credit history, and predict the likelihood of future loan repayment. In the realm of machine learning, classification techniques are essential for processing large volumes of data and generating accurate risk predictions. By combining the analytical rigor of financial mathematics with the predictive power of machine learning, this study aims to develop a robust and adaptive model for credit risk analysis one capable of responding to dynamic market and economic conditions, and improving the overall decision-making process in credit assessment.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Sarjono, H., Soeratin, B. S., & Rahmawati. (2025). Adaptive Credit Risk Modeling with Machine Learning and Financial Mathematics. Jurnal Ekonomi Dan Statistik Indonesia, 5(2), 234-243. https://doi.org/10.11594/jesi.05.02.01
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https://journal.mediapublikasi.id/index.php/oktal.
Amy Or, (2024), Growing Demand for Converti-ble Bonds Likely to Survive Rate Cuts, Bloomberg, 10 January, Retrieved from, https://www.bloomberg.com/news/articles/2024-01-09/growing-demand-for-convertible-bonds-likely-to-survive-rate-cuts
Aadi Gupta; Priya Gulati; Siddhartha P. Chakrabarty, (2022). Classification based credit risk analysis: The case of Lending Club, pp. 1-9. Retrieved from, Papers 2210.05136, arXiv.org.
Amin Nur Rais; Warjiyono Warjiyono, (2024), Sistem Informasi Prediksi Risiko Kredit Keuangan Berbasis Web Machine Learn-ing; Justika: Jurnal Sistem Informasi Akuntansi, 4(2), Desember, pp. 59-62. Doi: https://doi.org/10.31294/justika.v4i2.7509
Bart Baesens, Daniel Roesch, Harald Scheule; (2016), Credit Risk Analytics: Measure-ment Techniques, Applications, and
Examples in SAS, John Wiley & Sons, Oc-tober, 512 pages. ISBN: 978-1-119-14398-7
Banasik, J., Crook, J. N., & Thomas, L. C. (2003). Sample selection bias in credit scoring models. Journal of the Operational Re-search Society, 54(8), 822–832. https://doi.org/10.1057/palgrave.jors.2601560
Chong Ho Yu, Zhijuan Zhang, Janet Hanson, (2024), 2024 Machine Learning in Educa-tional Sciences, Springer Nature Singa-pore Pte Ltd. July. ISBN 978-981-99-9379-6 (eBook). https://doi.org/10.1007/978-981-99-9379-6
David J. Hand; Yinglin Yu, (2020), Machine Learning in Credit Risk Modeling; CRC Press.
Galindo, J., & Tamayo, P. (2000). Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications. Computational Economics, 15(1-2), 107–143. https://doi.org/10.1023/A:1008653714316
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Da-ta Mining, Inference, and Prediction. Sec-ond Edition, Springer. Retrieved from, https://link.springer.com/book/10.1007/978-0-387-84858-7
Heryono, H & Kardianawati, A. (2023). Imple-mentasi Metode Naive Bayes Untuk Klas-ifikasi Kredit Motor; JOINS (Journal of In-formation System), 3(1), pp. 10–21. Doi: 10.33633/joins.v3i1.1877Ruang Pub-likasi Ilmiah.
Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya; (2024), Model Klasifikasi Machine Learn-ing untuk Prediksi Ketepatan Penem-patan Karir, Jurnal Saintekom: Sains, Teknologi, Komputer dan Manajemen, 14(1). pp. 3-25
Jomark Pablo Noriega; Luis Antonio Rivera; José Alfredo Herrera, (2023), Machine Learning for Credit Risk Prediction: A Systematic Literature Review; Data 8(11), 169. 11 August, https://doi.org/10.3390/data8110169
Karimova, N. (2024). Application of AI in Cred-it Risk Scoring for Small Business Loans: A Case Study on How AI-Based Random
Forest Model Improves a Delphi Model Outcome in the Case of Azerbaijani SMEs; Retrieved from, https://doi.org/10.48550/arXiv.2410.05330
Lyn C. Thomas and David B. Edelman, (2017), Credit Scoring and Its Applications; Sec-ond Edition, SIAM Press, ISBN: 9781611974553.
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Re-search, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030
Malekipirbazari, M., & Aksakalli, V. (2015). Risk assessment in social lending via ran-dom forests. Expert Systems with Applica-tions, 42(10), 4621–4631. https://doi.org/10.1016/j.eswa.2015.01.002
Nicolas Suhadolnik; Jo Ueyama and Sergio Da Silva, (2023). Machine Learning for En-hanced Credit Risk Assessment: An Em-pirical Approach; Journal of Risk and Fi-nancial Management, 16: 496. https://doi.org/10.3390/jrfm16120496
Oluwabusayo Adijat Bello, (2023), Machine Learning Algorithms for Credit Risk As-sessment: An Economic and Financial Analysis; International Journal of Man-agement Technology, 10(1), pp.109-133. Doi: https://doi.org/10.37745/ijmt.2013/vol10n1109133
Stefan Jansen, (2018), Hands-On Machine Learning for Algorithmic Trading; O'Reil-ly Media, December, Packy Publishing. Retrieved from, https://www.oreilly.com/search/?q=author%3A%22Stefan%20Jansen%22&rows=100
Si Shi; Rita Tse; Wuman Luo; Stefano D’Addona; Giovanni Pau, (2022), Machine Learning-driven credit risk: a Systemic Review; Neural Computing and Applica-tions, 34(17), pp. 14327 – 14339, https://doi.org/10.1007/s00521-022-07472-2
Sihem Khemakhem, Younes Boujelbene (2017), Artificial Intelligence for Credit Risk Assessment: Artificial Neural Net-work and Support Vector Machines; ACRN Oxford Journal of Finance and Risk Perspectives; 6(2) pp. 1-17. ISSN 2305-7394
Shuochen Bi; Wenqing Bao, (2024), Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Manage-ment; International Journal of Global Economics and Management, 2(3). pp. 76-81. Doi: 10.62051/IJGEM.v2n3.08
Somesh Kumar Shukla; Ramakant Singh; Shruti Agrawal, Abhishek Dwivedi, (2025), Artificial Intelligence and the Fu-ture of Credit Scoring in FinTech: A Para-digm Shift; International Journal of Ad-vanced Research in Engineering and Sci-ence Management (IJARESM), DOI: 10.56025/IJARESM.2025.130225904IJARESM
Tolga Tavlas, (2018), Digital Banking: Strate-gies for Success, Palgrave Macmillan, ISBN: 9783319760385
Wulansari; Diana Purwitasari, (2023), Algo-ritma Random Forest pada Prediksi Sta-tus Kredit Usaha Rakyat untuk mengu-rangi Nonperforming Loan Rate; Jurnal of Intelligent System and Computation, 05(02), 2023. Doi: 10.52985/insyst.v5i2.358
Zairy Cindy Dwinnie, Rice Novita, (2024), Pen-erapan Machine Learning Pada Analisis Sentimen Twitter Sebelum dan Sesudah Debat Calon Presiden dan Wakil Presiden Tahun 2024; Jurnal Media Informatika Budidarma, 8(2), April. pp. 758-767. DOI: 10.30865/mib.v8i2.7504