An Integration of Decision Support System Using Classification Techniques and Time Series Analysis for Loan Disbursement in Financial Services

Abstract: The study designed, developed, and implemented a Decision Support System (DSS) that integrates Time Series Analysis and Forecasting techniques to enhance decision-making and loan disbursement efficiency within the financial sector. A quantitative research approach was utilized, employing the Rapid Application Development (RAD) model for building the system, alongside the Knowledge Discovery in Databases (KDD) process for data mining activities. Classification algorithms enabled the DSS component, while historical data were analyzed through time series methods for forecasting purposes. The classification model recorded an accuracy of 92.3%, while the forecasting component performed consistently well, with a Mean Absolute Percentage Error (MAPE) of 6.4%.

System performance and user satisfaction were assessed using the ISO/IEC 25010:2011 quality model, producing an overall weighted mean score of 4.26, reflecting a high level of user acceptance. The integration of classification and forecasting within the DSS contributed to improved efficiency of loan processing, more accurate decision outcomes, and enhanced operational procedures. To ensure the system's continued effectiveness and scalability, the study recommends ongoing model updates and retraining, improvements in system security, and the adoption of a scalable infrastructure capable of handling growing data volumes and user activity.

Decision Support System, ISO 25010, Integration, Loan Disbursement, Financial Services, Time Series Analysis, Forecasting

1. Abdalla, R., El-Diasty, M., Kostogryzov, A., & Makhutov, N. (Eds.). (2023). Time Series Analysis - New Insights. IntechOpen. doi: 10.5772/intechopen.100724 [Google Scholar] [Crossref]

2. 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]

3. Elseidi, M. Forecasting temperature data with complex seasonality using time series methods. Model. Earth Syst. Environ. 9, 2553–2567 (2023). https://doi.org/10.1007/s40808-022-01632-y [Google Scholar] [Crossref]

4. Fernández, I., Quislant, R., Gutiérrez, E., Plata, Ó., Giannoula, C., Alser, M., Gómez-Luna, J., & Mutlu, O. (2020). NATSA: A Near-Data Processing Accelerator for Time Series Analysis. IEEE 38th International Conference on Computer Design (ICCD). https://doi.org/10.1109/iccd50377.2020.00035 [Google Scholar] [Crossref]

5. Hou, R., Ye, X., Zaki, H. B. O., & Omar, N. a. B. (2023). Marketing decision support system based on data mining technology. Applied Sciences, 13(7), 4315. https://doi.org/10.3390/app13074315 [Google Scholar] [Crossref]

6. Fairlie, M. (2023, February 21). Decision support systems applications and uses. business.com. https://www.business.com/articles/decision-support-systems-dss-applications-and-uses/ [Google Scholar] [Crossref]

7. 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]

8. Kumar, A. (2023, May 4). Different types of Time-series Forecasting Models - Analytics Yogi. Analytics Yogi. https://vitalflux.com/different-types-of-time-series-forecasting-models/ [Google Scholar] [Crossref]

9. Lazzeri, F. (2020). Machine Learning for Time Series Forecasting with Python®. https://doi.org/10.1002/9781119682394 [Google Scholar] [Crossref]

10. Ma, C., Dai, G., & Zhou, J. (2022). Short-Term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5615–5624. https://doi.org/10.1109/tits.2021.3055258 [Google Scholar] [Crossref]

11. Maleki, M., Mahmoudi, M. R., Wraith, D., & Pho, K. H. (2020). Time series modelling to forecast the confirmed and recovered cases of COVID-19. Travel Medicine and Infectious Disease, 37, 101742. https://doi.org/10.1016/j.tmaid.2020.101742 [Google Scholar] [Crossref]

12. Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470. https://doi.org/10.1109/access.2020.2981905 [Google Scholar] [Crossref]

13. Meshref, H. (2020). Predicting loan approval of bank direct marketing data using ensemble machine learning algorithms. International Journal of Circuits, Systems and Signal Processing. https://doi.org/10.46300/9106.2020.14.117 [Google Scholar] [Crossref]

14. Moraffah, R., Sheth, P., Karami, M., Bhattacharya, A., Wang, Q., Tahir, A., Raglin, A., & Liu, H. (2021). Causal inference for time series analysis: problems, methods and evaluation. Knowledge and Information Systems, 63(12), 3041–3085. https://doi.org/10.1007/s10115-021-01621-0 [Google Scholar] [Crossref]

15. Nezhad, S. N., Zahedi, M. H., & Farahani, E. (2022). Detecting diseases in medical prescriptions using data mining methods. BioData Mining, 15(1). https://doi.org/10.1186/s13040-022-00314-w [Google Scholar] [Crossref]

16. Nikhil Madane and Siddharth Nanda (2019).Loan Prediction Analysis using a Decision Tree. Journal of the Gujarat Research Society [Google Scholar] [Crossref]

17. Park, S. H., Kim, H. S., Jhin, C., Kim, H., & Park, S. H. (2020). Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Computers and Electronics in Agriculture, 173, 105402. https://doi.org/10.1016/j.compag.2020.105402 [Google Scholar] [Crossref]

18. Park, W., Huh, JK. & Lee, JH. Automated deep learning for classification of dental implant radiographs using a large multi-center dataset. Sci Rep 13, 4862 (2023). https://doi.org/10.1038/s41598-023-32118-1 [Google Scholar] [Crossref]

19. Power, D. J., & Sharda, R. (2023). Decision support and analytics. In Springer handbooks (pp. 1401–1410). https://doi.org/10.1007/978-3-030-96729-1_66 [Google Scholar] [Crossref]

20. Sanjay. (2023, August 3). Decision Support Systems (DSS) and Group Decision Support Systems (GDSS). Geektonight. https://www.geektonight.com/decision-support-systems-dss-and-group-decision-support-systems-gdss/ [Google Scholar] [Crossref]

21. Sihombing, D. J. C. (2023). Development of construction project cost budget application using rapid application development method. iocscience.org. https://doi.org/10.35335/mantik.v7i3.4207 [Google Scholar] [Crossref]

22. Singh, Y. (2022, June 11). Using Confusion Matrix and Accuracy to Test Classification Models. Proclus Academy. https://proclusacademy.com/blog/explainer/confusion-matrix-accuracy-classification-models/ [Google Scholar] [Crossref]

23. Time series analysis. James D. Hamilton Princeton University Press. (2003). Wilmott, 2003(6), 92. https://doi.org/10.1002/wilm.42820030622 [Google Scholar] [Crossref]

24. Wahyuni, U. R., Fauziah, F., & Mardiani, E. (2020). Web-Based room loan management services using web based RAD method: Web-Based room loan management services using web based RAD method. Jurnal Mantik, 4(1), 452–459. https://iocscience.org/ejournal/index.php/mantik/article/download/772/518 [Google Scholar] [Crossref]

25. Wen, Q., Tian, Z., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2023). Transformers in Time Series: A Survey. International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/759 [Google Scholar] [Crossref]

• Advanced Digital Communication Strategies: A Comprehensive Analysis
• Developing an Explainable AI System for Digital Forensics: Enhancing Trust and Transparency in Flagging Events for Legal Evidence
• The Global Impact of Government Censorship on Women’s Access to Information: A Literature Review
• "Reimagining Higher Education Workspaces: A Review on The Transformative Role of Digital Technology Adoption"
• Improved CSP Efficiency: Innovations and Challenges in Thermal Energy Storage Systems