Tech Perspectives

New Decade, New Insights for Claims Operations

Gary Hagmueller

Chief Executive Officer of CLARA Analytics

AI Will Transform Claims Operations


Artificial intelligence (AI) will make an impact unlike anything the commercial insurance industry has experienced. This powerful workforce augmentation tool will transform operations by helping claims employees improve efficiency, lower loss costs and deliver better service.

The AI Effect

Many systems used in insurance are outdated and processes are highly manual. AI will change this by streamlining operations and absorbing routine tasks. Within the claims function, AI will use a far larger and more complete set of information to improve analytical fidelity and generate unbiased, more insightful recommendations than manual systems and processes. But there is an even bigger driver for AI adoption.

AI systems will take these vast troves of information and transform them into something meaningful and consumable. Effective AI can detect the large number of subtle factors (e.g., age or a claimant, where they live, what prescriptions they already take, which doctor they see, etc.) that may influence a particular outcome (the “weak signals”). The ability to identify weak signals within data – as well as when or how each may affect an outcome – is generally beyond human understanding or perception. For most people, once a data set exceeds five or 10 factors, humans lose the ability to interpret how those factors interrelate and influence each other. Given that we live in an era of ever-expanding data, AI presents a significant opportunity to find insights that generate more value than traditional analytics and intuition.

AI systems will absorb complexity and make it easier for human operators to spot relationships and why they matter. For example, a claimant may use certain words that do not raise concern in the course of a conversation with an adjuster. However, when those words are run through a machine learning model to identify sentiment and paired with a model that predicts the expected activity flow, the machine is capable of identifying underlying frustrations that may lead the claimant to escalate matters. An adjuster can then work with the claimant to address concerns while reducing the cost of the claim. This ability to improve outcomes through data is why AI spending is projected to reach $77.6 billion by 2022.

What does AI means for claims teams?

Claim handlers soon will become instant experts, understanding what’s happening within a claim at a deeper level thanks to AI. Information will guide how those workers make decisions and where they direct energy. Given the speed of processing, AI systems can place such actionable data at an adjuster’s fingertips exactly when it is needed. It can also be paired with recommended actions and prompts to solve problems before they are even recognized. AI will spot larger, recurring issues and trends, prompting proactive changes within organizations and driving virtuous improvement cycles across the broader industry as well.

Because loss adjusters will be equipped with data-driven insights, they will help more companies faster, accurately answering questions and directing them to the best resources (doctors, mechanics, lawyers, etc.). By sending a higher volume of people to resources that resolve issues rapidly and with consideration of actual needs, escalations will be reduced, unnecessary procedures will be eliminated, and legal claims will be prevented.

And this is only the beginning …

What can you do today to prepare?

  • Be focused on a specific application – rather than blue sky thinking – to increase the likelihood of success. A good starting point might include a claims portfolio review to identify at-risk claims and intervene before they escalate.
  • Bring on board stakeholders by demonstrating a strong value proposition and predicted impact. E.g., by using AI to track claims in Workers’ Compensation programs, companies can identify high-risk claims and avoid costly litigation that increases claim costs, often greater than 400%.
  • Recognize that data will never be perfect. The trick is to just start. Data can be remediated over time as experience is gained with machine learning applications and teams are able to better interpret and solve data challenges.

About the Author

Gary Hagmueller, chief executive officer of CLARA analytics, has been a leader in the technology industry for more than 20 years, with a deep focus on building artificial intelligence and machine learning applications for the enterprise market. Gary holds an MBA from the Marshall School of Business at the University of Southern California as well as a bachelor’s in Business Administration from Arizona State University. For more information, visit and follow CLARA analytics on LinkedIn, Facebook and Twitter.