Advisers

Rommel J. Constantino, DIT

Discipline

Information systems

Abstract

While microfinance institutions primarily focus on financial services such as loan distribution and savings management, efficient human resource (HR) management is equally critical to their success. This includes hiring qualified personnel, facilitating employee development, and monitoring performance. Despite the use of digital recruitment platforms by organizations like Ascenders Business Services, challenges remain in expediting recruitment processes and tracking employee progress. This project addresses these issues through the development of a data-driven, AI-enhanced Human Resource Management System that supports recruitment, skill development, and performance analytics. The system was developed using the Agile Scrum methodology, with the team working in iterative sprints to plan, design, and test features incrementally. Web-based tools were used for the user interface, while PyTorch powered the predictive analytics components. The system comprises three main modules: Skill Development, Recruitment Analytics, and HR Data Analytics. Requirements were gathered through interviews with HR representatives from Ascenders Business Services. System functionality was validated through manual and automated testing using sample datasets and predefined test cases. The implementation of the system enabled HR personnel to monitor employee training and skill acquisition, assess applicant suitability based on historical data, and anticipate future skill requirements. It generated insightful reports to assist managers in team evaluation and strategic planning. System testing confirmed its capability to perform accurately in realistic conditions, reducing recruitment time and improving data interpretation. The integrated system collects and analyzes both employee and applicant data across its three modules. The Skill Development module tracks training milestones, while the Recruitment Analytics module uses AI to evaluate candidate qualifications and provide hiring recommendations. The HR Data Analytics module interprets performance metrics to support managerial decisions. Together, these modules enhance efficiency, reduce errors, and promote data-driven HR practices, offering significant value to microfinance institutions seeking to optimize workforce management.

Keywords

predictive analytics, human resource management, microfinance, recruitment analytics, hr data analytics, employee performance tracking