Machine Learning Applications in Forecasting Loan Disbursement Patterns

Forecasting loan disbursement is essential for financial institutions to manage liquidity effectively and mitigate risks. Traditional forecasting methods often struggle with the complex and seasonal dynamics present in financial data. This study compares several forecasting models including ARIMA, Linear Regression, Long Short-Term Memory (LSTM) networks, and Prophet using seven years of loan disbursement data. Results indicate that LSTM networks produce the most accurate forecasting. The findings show the potential of machine learning to improve planning and decision-making for lenders, while maintaining strict considerations for data privacy and fairness.

Forecasting, Loan Disbursement, Machine Learning, Financial Services, Time Series

1. Auffarth, Ben (2021). Machine learning for time-series with Python: forecast, predict, and detect anomalies with state-of-the-art machine learning methods. Packt Publishing [Google Scholar] [Crossref]

2. Bandara, K., Hyndman, R., & Bergmeir, C. (2022). MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns. International Journal of Operational Research, 1(1), 1. https://doi.org/10.1504/ijor.2022.10048281 [Google Scholar] [Crossref]

3. Dama, F., & Sinoquet, C. (2021). Time Series Analysis and Modeling to Forecast: a Survey. arXiv (Cornell University). https://arxiv.org/pdf/2104.00164.pdf [Google Scholar] [Crossref]

4. Khan, F. U., & Gupta, R. (2020). ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India. Journal of Safety Science and Resilience, 1(1), 12–18. https://doi.org/10.1016/j.jnlssr.2020.06.007 [Google Scholar] [Crossref]

5. Nelson, D. M., Pereira, A. C. M., & de Oliveira, R. A. (2017). Stock market’s price movement prediction with LSTM neural networks. In International Joint Conference on Neural Networks (IJCNN) (pp. 1419–1426). [Google Scholar] [Crossref]

6. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PloS One, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889 [Google Scholar] [Crossref]

7. Prajapati, S., Swaraj, A., Lalwani, R., Narwal, A., Verma, K., Singh, G., & Kumar, A. (2021). Comparison of traditional and hybrid time series models for forecasting COVID-19 cases. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-493195/v1 [Google Scholar] [Crossref]

8. Schaffer, A. L., Dobbins, T., & Pearson, S. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Medical Research Methodology, 21(1). https://doi.org/10.1186/s12874-021-01235-8 [Google Scholar] [Crossref]

9. Wahedi, H., Wrona, K., Heltoft, M., Saleh, S., Knudsen, T. R., Bendixen, U., Nielsen, I., Saha, S., & Borup, G. S. (2022). Improving accuracy of time series forecasting by applying an ARIMA-ANN hybrid model. In IFIP advances in information and communication technology (pp. 3–10). https://doi.org/10.1007/978-3-031-16407-1_1 [Google Scholar] [Crossref]

10. Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0 [Google Scholar] [Crossref]

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