MICROFINANCE MANAGEMENT SYSTEM: ADMINISTRATIVE (USER MANAGEMENT, LEGAL MANAGEMENT, COMPLIANCE ASSURANCE, RISK ASSESMENT) PREDICTIVE RISK ANALYSIS USING LOGICAL REGRESSION MODEL

Abstract

A Microfinance Management System (MMS) functions as the administrative core of microfinance institutions (MFIs), which offer financial services to individuals and small businesses without access to conventional banking. This system streamlines key operations such as loan processing, savings account management, and client tracking, thereby enabling MFIs to serve low-income clients more effectively. Through automation of administrative tasks and accurate data management, the MMS contributes to greater transparency and operational efficiency. The system was developed using the Agile Scrum methodology, a project management framework based on incremental and iterative development. Each iteration was carried out in two- to four-week sprints, focusing on building essential features first. At the end of each sprint, a potentially shippable product was delivered. Continuous stakeholder and customer feedback informed improvements and guided subsequent development cycles. This flexible and adaptive approach ensured the product evolved to meet business and user needs effectively. The implementation of the MMS with administrative functions—specifically User Management, Legal Management, Compliance Assurance, and Risk Assessment—brought significant benefits to the partner organization. The integration of a logical regression model enabled predictive risk analysis, contributing to more data-driven decision-making. The project demonstrated the value of artificial intelligence in supporting practical applications within the microfinance sector, particularly in enhancing risk evaluation and administrative efficiency. This project is highly relevant, as it addresses a critical shortfall in microfinance operations: the absence of effective risk management systems. Many MFIs face challenges such as high loan default rates, administrative inefficiencies, and limited impact on poverty alleviation. By integrating predictive analytics and structured administrative modules, the system offers a scalable solution that can benefit various stakeholders in the microfinance ecosystem, improving institutional resilience and service delivery.