Modeling Online Loan Risk Dynamics with SEIQRL, Regulation, and Financial Literacy Analysis in Indonesia

  • Christari Lois Palit Universitas Negeri Manado
  • Agresia Servina Sabubun Universitas Negeri Manado
  • Dyrel Samuel Kusnadi Universitas Negeri Manado
  • Julius Zendrato Universitas Negeri Manado
  • Mikha Rajagukguk Universitas Negeri Manado
  • Tessy Angraini Mokodanga Universitas Negeri Manado
Keywords: Online Lending, SEIQRL, Regulation, Financial Literacy, Basic Reproduction Number

Abstract

This study examines the dynamics of risky online lending behavior in Indonesia using the SEIQRL mathematical model. The model extends the SEIR approach by adding Quarantined (Q) and Literate (L) compartments to represent regulatory intervention and financial literacy. This study aims to construct the SEIQRL model, analyze the risk-free equilibrium point, calculate the basic reproduction number (R₀), and evaluate the effects of regulation and financial literacy on the spread of risky online lending behavior. The method uses a quantitative approach based on mathematical modeling and numerical simulation with Wolfram Mathematica. The results show that the baseline scenario produces an R₀ value of 6.1538, indicating that risky online lending behavior can spread strongly within the population. Strong regulation and strong literacy scenarios reduce the risk, but they do not fully control its spread. The combined regulation and literacy scenario produces an R₀ value of 0.8696, meaning the risk can be controlled because R₀ < 1. These findings show that controlling risky online lending cannot rely only on access restrictions or service blocking. Early financial literacy must also serve as a preventive strategy. The SEIQRL model can support policy analysis for controlling online lending risks in Indonesia.

References

Brauer, F., Castillo-Chavez, C., & Feng, Z. (2019). Mathematical models in epidemiology. Springer. https://doi.org/10.1007/978-1-4939-9828-9

Chen, X., & Ghosh, D. (2020). A mathematical model for online P2P lending with financial regulation. International Journal of Financial Studies, 8(2), 33. https://doi.org/10.3390/ijfs8020033

Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990). On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology, 28(4), 365–382. https://doi.org/10.1007/BF00178324

Dorfleitner, G., Hornuf, L., Schmitt, M., & Weber, M. (2017). FinTech in Germany. Springer. https://doi.org/10.1007/978-3-319-54666-7

Financial Services Authority. (2022). Financial Services Authority Regulation No. 10/POJK.05/2022 on Information Technology-Based Crowdfunding Services.

Financial Services Authority. (2024). 2024 National Survey on Financial Literacy and Inclusion. Financial Services Authority.

Financial Services Authority. (2025). 2024 Annual Report: Stability of the financial services sector and the development of P2P lending. OJK Press.

Gao, H., Zhu, H., & Ma, H. (2024). Peer effect and funding success: Analyzing friendship networks in online credit markets. Finance Research Letters, 66, 105683. https://doi.org/10.1016/j.frl.2024.105683

Hasan, I., Horvath, R., & Mares, J. (2022). Finance and wealth inequality. Journal of International Money and Finance, 124, 102627. https://doi.org/10.1016/j.jimonfin.2022.102627

Ho, K. C., Shi, Y., & Zhang, Y. (2024). Peer effects in the online peer-to-peer lending market: Ex-ante selection and ex-post learning. Journal of Financial Markets, 92, 100572. https://doi.org/10.1016/j.finmar.2023.100843

Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118

Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5

Tang, B., Wang, X., Li, Q., Bragazzi, N. L., Tang, S., Xiao, Y., & Wu, J. (2020). Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine, 9(2), 462. https://doi.org/10.3390/jcm9020462

Van den Driessche, P., & Watmough, J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180(1–2), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6

Zhao, Y., Li, J., Wang, X., & Ma, L. (2021). Modeling credit risk contagion in internet peer-to-peer lending platforms using complex network theory and epidemic dynamics. Physica A: Statistical Mechanics and its Applications, 565, 125568. https://doi.org/10.1016/j.physa.2020.125568

Published
2026-06-28
Section
Articles