Machine learning in the cloud: A sound policy for property and casualty insurers

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As the insurance industry faces shrinking revenues and declining profits, property and casualty (P&C) carriers should find new ways to compete. Lately, P&C carriers have focused on system transformation projects to upgrade various systems in the hopes of improving data quality. While these projects sometimes provide short-term benefits, insurers often miss long-term revenue opportunities. In our view, predictive analytics and machine learning (ML) technology are key to future success.

Machine learning and cloud in insurance - PwC

Cloudy with a chance of AI

At times, taking the simpler, ‘clear’ path isn’t always necessarily the right choice. With insurers looking for fast and easy answers, embracing technologies like artificial intelligence (AI) and the cloud is the best long-term solution to outpace the competition and avoid the storm of problems property and casualty (P&C) insurers now face.

We know you’re concerned

We’ve heard some skepticism from insurers who have worked successfully with analytical tools for years to analyze risks and opportunities. We’ve also heard clients express reservations about data security and privacy, regulation, or even resistance from their IT teams. But, the machine learning (ML)/cloud buzz isn’t just hype, and a growing number of carriers have found ways to mitigate the technology’s risks. Many skeptics now find themselves lagging behind early adopters.

Turning to machine learning

Leading P&C insurers have successfully implemented ML across various business applications. Here are some ways they have used the technology in their businesses:

  • Enhancing products, pricing, and underwriting
  • Strengthening the claims process
  • Predicting and preventing fraud
  • Enhancing customer service and billing
  • Using the cloud to boost machine learning results

Where to go from here

The next step is to get started. Begin thinking about how your business can embrace the technology at your disposal. Use our five key steps for success as a starting point:

  1. Understand what you are working with
  2. Consider a data audit
  3. Think about what you want
  4. Go to the cloud
  5. Find your competitive edge

What is stopping CEOs from adopting ML & cloud?

Considerations to help manage these concerns

“I’m concerned about data security and privacy. While cybersecurity risks are real, they often point out gaps in access controls and governance rather than where data sits.
“We could face resistance from our IT team. Maybe. Frankly, change management issues around any new technology are often harder than the technology itself. Plan accordingly. You may need to train, or hire, a new kind of IT worker.
“We can’t rely so heavily on one infrastructure vendor.” Some firms spread activity across multiple vendors. Technical solutions exist that now make it easier to shift work quickly across platforms.
“We can’t afford any performance issues. Today’s cloud providers often offer services that are more responsive, updated more frequently, with flexible architectures to handle even extreme use cases. Define what you really need and find the vendors that fit.
“I’m not sure regulators would support such a move.” Many regulators are getting on board with these changes. Often, the key is proactive communication and a more comprehensive view of managing risks.

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How PwC can help

Our teams in asset and wealth managementbanking and capital markets and insurance are helping our clients tackle the biggest issues facing the financial services industry. With professionals across tax, assurance and advisory practices, we can help you find ways to thrive even in a period of uncertainty. Whether you're preparing for regulatory changes, putting FinTech/InsurTech to work or rethinking your human capital strategy, we work together with you to deliver value to your business.

For more information on how PwC can help, reach out to one of our leaders below or explore our data analytics services.

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Anand Rao

Global & US Artificial Intelligence and US Data & Analytics Leader, PwC US

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