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RPA & AI: Are They Still the Right Answers for Insurance Companies?

Tomasz Balcerek

Director, Account Management

Stas Chulsky

Senior Manager, Intelligent Automation Consulting
Blog
  • Insurance

By now, most insurance companies have tested a range of robotic process automation (RPA) and artificial intelligence (AI) solutions with varying rates of success, but they are no longer the standalone tools or top implementation priorities that they once were for these organizations. Instead, they are now most often seen as individual components of the overall automation initiative that contribute to the end-to-end digitization and automation defined under hyperautomation

Forrester reports that RPA and AI will deliver a value of $134 billion by 2022*. With the speed and accuracy of data processing at the heart of insurance business—from not only a risk management but also a customer experience perspective—these solutions are here to stay. The question then becomes, how can insurers most effectively leverage RPA and AI to further themselves on their hyperautomation journey?

Given that the ultimate goal for modern insurers is hyperautomation, RPA and AI must be staged into the digitization and automation journey, as they’re essential to addressing insurance carrier needs—customer acquisition and retention, policy administration, claims operations, and risk and fraud management—with hyperautomation. To keep moving forward on this journey, it’s important for insurers to define how to use automation for its intended purpose and scale it to fit with the wider organizational perspective. 

Examining What You’ll Need to Fully Leverage RPA

If you still rely on RPA only to streamline basic processing activities, it likely means that your underlying systems and operations require modernization in order to reach your desired state of automation. Insurers at this level of RPA maturity are often faced with a number of different challenges, including:

  • Disintegrated systems and platforms that require manual intervention to capture and transfer policy and claims data
  • Lack of standardization in both technology and operations
  • Disparate policy and claims data entry from various channels (paper forms, calls to customer center, self-service applications)
  • Planning for major systems upgrades and modernization in the future whilst there is an immediate need for process improvements
  • Disjointed workflows and antiquated business processes

The ideal approach to the above challenges would be to completely redesign, integrate and modernize your platforms and processes. Before doing so, there are many points to consider:

  • Prototyping before embarking on the full end-to-end transformation journey
  • Testing automation scenarios to validate your expected business outcomes and ROI as well as pure applications testing
  • Managing cultural change towards new, modern ways of working with design thinking
  • Budgeting challenges versus the need for short-term improvements
  • Coalescing organizations and systems as a result of company mergers and acquisitions
  • Automating standard reporting and controls in claims and policy administration
Examining Which AI Solutions to Prioritize

While RPA is most effectively used to augment business processes, AI is needed to enhance the customer experience, operational efficiencies, data quality and analytics for quicker and more accurate decisions in claims eligibility, fraud detection and risk management. Although an insurer might have already modernized and implemented best-in-class technical solutions, they can still leverage the strength of AI to gain more benefits from that investment and advance toward hyperautomation. Insurance carriers should therefore consider prioritizing AI solutions, such as:

  • Customer interaction models and tools to support an excellent customer experience
  • Scaling of operations to gain flexibility and accommodate peak seasons via transaction grouping and prioritization
  • Claims intake and mailroom operations with automatic indexing and processing
  • Claims processing and eligibility decisions with automatic adjudication
  • Policy administration in records updates and communication
  • Data cleansing
  • Fraud detection based on suspicious activity monitoring
  • Data analytics for risk management 
Conclusion

To summarize, it is undoubtedly still an imperative to evaluate RPA and AI solutions as crucial components of your wider digitization and modernization journey. Doing so will not only speed up your transformation by eliminating short-term processing waste via RPA, but help you leverage AI to capitalize on already-made investments. And, in most cases, both work in tandem to optimize the resulting process benefits. Often, the first step in your automation journey is to find a technology partner to evaluate your bottlenecks and options, and implement fit-for-purpose, pragmatic solutions.

*”Intelligent Automation (RPA Plus AI) Will Release $134 Billion In Labor Value In 2022”, Forrester, February 21, 2020.

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