COVID-19’s Impact on Data Governance and Risk Management Across Financial Services

Managing and monitoring credit, market, liquidity, and operational risk across financial markets was hard enough with ongoing geopolitical tensions, international trade wars, and of course the occasional hurricanes and earthquakes. The current pandemic is forcing chief risk officers and their teams to recalibrate old assumptions and models used to manage and monitor risk. Innovations in cloud computing, machine learning (ML)-enabled predictive modeling solutions, and artificial intelligence (AI)-led business process automation solutions will help reshape how firms manage risk in the new world.

At the same time, these innovations and an abundance of data will provide opportunities to understand and grasp new business opportunities as they emerge. For example:

• Life insurance companies can capture real-time hospitalization, infection, and death related data from the Centers for Disease Control and Prevention for analysis with traditional demographic and claims data. This allows them to improve how they forecast the financial impact of life insurance claims from policy beneficiaries, while at the same time offering new services to those beneficiaries to become new clients.  
• Banks can integrate massive volumes of unstructured data from call and web logs into a cloud data lake with traditional transactional and account data. This enables AI-powered analytics applications to combat fraudulent activities. Teams can also leverage insights from those applications to predict next best offers and actions to ensure their top customers are provided the best customer experiences.

But success will depend on chief data officers and chief information officers making the right investments to operationalize data governance and data management to ensure delivery of fit-for-use data and insights at an enterprise level. Operationalization focuses on four key areas:

• Automating data quality management vs. dealing with data errors by throwing bodies at the problem manually
• Establishing end-to-end data lineage and transparency with an enterprise data catalog
• Centralizing master and reference data management vs. dealing with it in a distributed and ungoverned manner
• Enabling self-service data governance and access to avoid unnecessary IT bottlenecks through traditional data access and retrieval requests

What does it take to achieve this? What pitfalls should you avoid? To learn more about this topic, read our eBook on “Operationalizing Data Governance for Enterprise Risk Management in Financial Services.”