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

Paula Joy L. Dela Cruz, Keno C. Piad, Isagani M. Tano, Ace C. Lagman, Joseph D. Espino, Jonilo C. Mababa and Jayson M. Victoriano
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Paula Joy L. Dela Cruz: La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines
Keno C. Piad: La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines
Isagani M. Tano: Quezon City University, San Bartolome, Quezon City, Philippines
Ace C. Lagman: Far Eastern University, Sampaloc, Manila, Philippines
Joseph D. Espino: La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines
Jonilo C. Mababa: La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines
Jayson M. Victoriano: Bulacan State University, Bulihan, City of Malolos, Bulacan, Philippines

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 7, 149-160

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.

Date: 2025
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