Unveiling the Future: Predicting SACCOS Continuity through Machine Learning Algorithms

Abstract

Savings and Credit Cooperative Societies (SACCOS) play a pivotal role in bridging the financial access gap for underserved populations, particularly in rural and low-income communities. By mobilizing savings and extending affordable credit, SACCOS contribute significantly to socioeconomic development, financial empowerment, and community resilience. However, the growing complexity of member behaviors, coupled with the dynamic nature of financial ecosystems, presents considerable challenges in ensuring long-term continuity. As Information and Communication Technologies (ICT) become increasingly embedded in SACCOS operations, there is a pressing need for intelligent data-driven tools to support predictive decision making and strategic planning. This study addresses this gap by employing machine learning (ML) algorithms to forecast critical indicators of SACCOS sustainability, namely member dropout rates, savings trends, and financial product utilization. Using Design Science Research Methodology (DSRM), A predictive model based on real-world SACCOS data were systematically developed. The Random Forest algorithm was employed for feature selection, using a 0.02 importance threshold to identify the most influential variables across the three prediction domains. Subsequently, seven machine learning models were trained and evaluated to determine the optimal predictive engine for each outcome. The results demonstrate the powerful potential of machine learning to capture subtle patterns in member and financial behavior, thereby enabling proactive interventions to mitigate risks and enhance operational sustainability. Accurate predictions not only support timely decision-making but also guide strategic efforts in product design, investment allocation, and member retention. Ultimately, this study contributes a robust and scalable framework for harnessing data science to safeguard the continuity and resilience of SACCOS in an increasingly digitized and competitive financial landscape.