Data-Driven Risk Management in Universities: Application of Analytics and Predictive Models
Authors
- Nazgul Davletbayeva Karaganda National Research University named after academician Ye.A. Buketov, Karaganda, Kazakhstan
-
Torgyn Nurseit
Buketov Karaganda National Research University, Karaganda, Kazakhstan
-
Kuat Zhetpisbayev
Buketov Karaganda National Research University, Karaganda, Kazakhstan
-
Aktam Burkhanov
Tashkent State University of Economics, Tashkent, Uzbekistan
-
Aigerim Rakhmetollayeva
Buketov Karaganda National Research University, Karaganda, Kazakhstan
DOI:
https://doi.org/10.31489/2026FEB1/1393Keywords:
Data-driven risk management, Universities, Predictive models, Learning analytics, Enterprise risk managementAbstract
Modern universities operate in an environment of increasing uncertainty caused by digital transformation, financial instability, demographic change, competition for students and academic staff, cybersecurity threats, and rising expectations from regulators and society. Traditional risk management in higher education often remains fragmented, reactive, and insufficiently connected with strategic decision-making. The purpose of this study is to develop a conceptual and applied framework for data-driven risk management in universities through the integration of enterprise risk management, key risk indicators, dashboards, learning analytics, and predictive models. The study is conceptual-applied in nature and is based on a review of international literature on enterprise risk management, learning analytics, early warning systems, predictive analytics, and digital governance in higher education. The methodology includes system analysis, comparative analysis, risk mapping, maturity assessment, and conceptual modelling. The results propose an integrated data-driven ERM framework consisting of five components: risk data architecture, risk taxonomy, key risk
indicators, predictive models, and governance response mechanisms. The study also presents illustrative risk
indicators, risk thresholds, a risk scoring matrix, and examples of predictive model application for academic,
financial, digital, human resource, operational, and reputational risks. The findings show that analytics and
predictive models can transform university risk management from a formal control function into a proactive, measurable, and evidence-based governance system. The practical significance of the study lies in the possibility of applying the proposed framework in higher education institutions, including universities in Kazakhstan, to improve risk identification, early warning, strategic planning, and institutional resilience.
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