Doctoral Dissertation Research in DRMS: Building a comprehensive understanding of enterprise risks and their interdependencies for improved risk-intelligence

Businesses are ubiquitous and inseparably merged with human lives. Thriving corporations provide the fundamental activity necessary for an equitable society with benefits ranging from the ready availability of goods and services to regional employment in support of economic prosperity. Simultaneously, high-risk events such as COVID‐19 are bleak reminders that enterprises are continuously threatened, and that building enterprise resilience is crucial.

Yet, one of the cornerstones of enterprise resilience - risk intelligence – or the adequate awareness of the various risk factors and their complex interdependence, remains grossly underdeveloped. This project addresses this gap in knowledge by developing complex network views of enterprise risks. Employing big data and tools of artificial intelligence the project identifies and develops an enterprise-agnostic risk inventory that is considerably more comprehensive than any such publicly available resource.

In addition, expert input allows for converting seemingly disconnected risk factors into interconnected complex risk networks, which enable the search for risk chains that may compound and lead to more significant adverse effects. This work builds a knowledge resource base useful to explain mechanisms of cascading risks and to predict the varying impacts of risk events on enterprises.

Thus, the work serves national interest and is in alignment with NSF’s mission to promote the progress of science, and via that, advance national prosperity and welfare.

This work is grounded in the complexity systems view of enterprise risk management and seeks to build a comprehensive, data‐informed view of the dynamic risk network influencing enterprises to systematically enhance risk awareness and contribute toward the evolution of truly risk‐intelligent organizations. This perspective is achieved through: a) the development of a comprehensive enterprise agnostic risk factor inventory, b) the generation of risk networks that map risk factor interrelations, and c) the exploration of the complex dynamics of these risk networks.

The work entails a mixed-methods approach utilizing information extraction (IE) on a large, curated dataset of company risks, and Fuzzy Cognitive Mapping (FCM) for complex risk-network development and analyses. Public information (SEC filings) are augmented with private risk data (analyst reports) for enterprises in the S&P 500, providing robust coverage of true risk factors.

The corpus is analyzed using IE principles, which include part‐of‐speech tagging, dependency-parsing, n‐gram extraction, and topic modeling. Surveyed and/or interviewed experts from industry and academia inform qualitative measures of risk interaction (dependencies, direction, and degree of influence) during the FCM process leading to complex risk network development.

The resulting risk networks are analyzed quantitatively to reveal insights such as centrality of risks, the distances between risks, and sub‐group structures within the risk networks that could inform an order of critical risks. The research contributes to the field of Enterprise Risk Management (ERM) by increasing scholarly awareness on the breadth of risks affecting enterprises.

Further, via FCM, the work converts expert understanding into a quantifiable network, bringing focus on risk interdependencies. In addition, via network analysis, the effort illuminates critical risks and propagation mechanisms that may be overlooked in traditional views. Overall, this effort provides an expansive, data‐informed view of risk factors affecting enterprises, their (non-intuitive) interactions, and related dynamics thereby advancing the complexity-view of ERM research as well as sharpening an enterprise’s ability to predict cascading effects caused by seemingly unrelated events.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.