The COVID-19 pandemic has had a significant impact on the economy at all levels, from global markets to micro-enterprises. At the same time, the pandemic and its consequences have left a wide digital footprint. Its study seems to be extremely relevant, since approaches to the analysis of the digital footprint of a pandemic and the use of its results for risk management can be successfully applied in the event of similar threats. The relevance of the problem is also recognized by economists who note the significant impact of the pandemic on the economy and economic theory in general. The aim of the study is to develop approaches to the rapid quantitative assessment of the impact of the pandemic on the university based on the data of accounting financial systems, their testing and generation of proposals for minimizing the risks of financial and economic activities. The scientific hypothesis of the study is that based on the analysis of data transmitted to the social insurance fund on the disability of employees, the effectiveness of risk management of financial and economic activities in a pandemic at the university level can be improved. Growth in efficiency is ensured by adjusting plans to minimize risks, taking into account the heterogeneity of the impact of the pandemic on employees depending on age, gender, and belonging to the category of personnel. For data integration and analysis, the authors used Data Science approaches. Using the data of Ural Federal University as an example, the information content of the analyzed data is shown and what management decisions to minimize risks can be made on their basis. An approach to the quantitative analysis of the impact of the pandemic on employees of a legal entity is proposed. The effectiveness of using distance learning to counter the pandemic, the vulnerability to the pandemic of certain categories of employees, the gender structure of disability are demonstrated. The theoretical significance of the work lies in the development of approaches to the use of financial information to improve risk management. The information obtained can be applied in practice, in particular, to clarify the calculation of reserves, improve technical specifications in the development of information systems.