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Still Standing, Still Learning, and Still at Home

We accurately predicted that data, AI, and cloud experiences would be the new normal expectation during COVID-19 from leadership, shareholders, employees, vendors, and customers. And we double down on that prediction for 2022 and beyond.

This pandemic is starting to end -- but it isn't over.

Critical progress has been rapidly made in the front, middle, and back offices while creating workspace social distancing.

The cloud is office-less and in some cases people-less. The dynamic and interconnected parts of the world are complex. Look for more automation of multiparty exchanges of information and engagement for complex interconnections in the insurance value chain while simple interactions disappear to self-managed.

What insurers should do now:

  • Pay attention to your local “virus weather” conditions and forecasts.
  • Create and act on an analytics strategy that consolidates your gains while tying in the lessons we are still learning in this moment in history.
  • Link customer needs and business outcomes with hubs, dashboards, maps, models, analytics, decisions, and feedback.
  • Embrace remote data collection by first parties, robotic services, remote sensors, and vendors. And consider what it means to require the internet as an essential service for employees, customers, and vendors.
  • Widespread adoption of digital technology will help you reduce costs and add resiliency to your business – especially in property lines.
  • Create data policy, procedure, and governance to manage all sorts of new surveillance, health, safety, prevention, and compliance assets.
  • Reinspect your model risk management inventory and check all current production models for any data, parameter, or trending concerns as the changes in behaviors of people and businesses have some profound deviations from the past and statistical/actuarial models may lapse in performance while financial forecasting models in general are at risk.
  • Prepare to future proof your operations from social fairness impacts on how data are used, and decisions are made, by AI models, people, and people using AI models. Monitoring and auditing are first steps before mitigating and managing data, processes, and models in production.

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