AI as Augmentation Tool: Leveraging GenAI to Improve Human Decisioning

The technology is at its best when utilized as an augmentation tool, and as an industry, we should be focused on maximizing it to complement and better inform human decisions.

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The question on every organizational leader’s mind these days seems to be how their business can best leverage the power of Generative AI (GenAI). As companies across industries are implementing GenAI solutions to improve operational efficiency and stay ahead of the competition, one of the primary reasons that the insurance industry is turning to AI-powered automated solutions is to refine and improve the claims process. As exciting as this new technology and its capabilities may be, it’s important at this stage to resist the urge to completely relegate decisioning to GenAI. The technology is at its best when utilized as an augmentation tool, and as an industry, we should be focused on maximizing it to complement and better inform human decisions.

Keep Humans in the Loop to Avoid Bias and Inaccuracy.

Artificial Intelligence has been around for many years, but the mass availability of GenAI has moved the capabilities into mainstream society and majorly increased its visibility. We’re seeing a real tipping point right now in terms of what can be done with these tools and how they can improve the human ability to iterate and produce better outcomes. This is indeed exciting, but it’s important not to race ahead of ourselves.

AI helps humans amplify our productivity to iterate, and it can even lift redundant processes and heavy research processes off of individuals. But despite its many positive attributes, GenAI has also shown some areas for concern. Bias can be a significant problem, causing the technology to generate prejudiced or inaccurate results. Though this can in theory be trained out, careful review is essential to ensure errors and biases do not slip through. Human proofing of AI-generated content (or keeping the “human in the loop”) is the best way to ensure that biased results or general mistakes are identified and addressed in order to avoid inaccurate results. This is especially critical when we consider how the results might impact an individual and that it is within our abilities and responsibilities to ensure accuracy and fairness.

Utilize GenAI to inform the way claims managers do their jobs.

Anything that involves understanding and analyzing large volumes of data is a great use case for Generative AI. One powerful example is AI’s ability to examine complex information sets and root out the relevant data to predict claims outcomes. When used in this manner, GenAI can help claims professionals make more informed decisions quickly and even be more productive.

For example, GenAI can help us write better triaging questions and walk the policyholder through the First Notice of Loss (FNOL) process. We can also use artificial intelligence and machine learning to examine FNOL data to help us better triage claims. The technology can even be utilized to make a preliminary estimate. But once the AI has compiled and written out all the information into a preliminary estimate, a human adjuster should step in to refine and correct it as needed.

By removing some simpler and more monotonous tasks, AI can save the adjuster valuable time that can then be used to focus on more complex claims. The key here is that the technology is helping to inform the way that the adjuster manages processes by helping them to be better informed and make better decisions, all without taking the decisioning completely away from the adjuster.

Maximize GenAI to enhance the customer experience.

Customer experience is an especially good use case for GenAI, which excels when it comes to activities like automating responsiveness or providing status updates to customers. Algorithms can be created to give detailed answers to policyholders’ most frequently asked questions, so that instead of providing a generic preloaded answer, GenAI can answer more specific questions with increased granularity based on the individual. In this way, customers can get quick, more personalized answers to questions without having to contact their insurance providers.

Despite these obvious advantages of AI in customer service, some claim scenarios still need to be handled by empathetic adjusters who have the capacity to make a human connection with policyholders. AI can enhance the customer experience in some instances but in others, nothing can replace the human touch that policyholders expect from quality customer support.

The speed and responsiveness of GenAI technology make it invaluable in its ability to shape this experience as well. GenAI tools can help customer service representatives (CSRs) ask better questions—and based on the data that’s being given, these tools are able to inform CSRs during the conversation about data points the policyholder has mentioned that they should ask about. Rather than swapping out human representatives for digital replacements, these generative AI-based triggers and cues are helping humans do their best work.

Artificial Intelligence Complements Human Intelligence

We’ve barely scratched the surface of GenAI’s capabilities, but it’s already clear that AI can help drive customer experience, drive better touch points with the customer, provide more instant information, and help us shape better questioning through the reporting process as well. When utilized as a complementary technology, GenAI is an expedient and exciting path to well-equipped and well-informed adjusters, and to happy policyholders.

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Ken Tolson // Ken Tolson is the CEO of Digital Solutions at Crawford & Company, a leading claims management firm. In his current role, Ken oversees Crawford’s digital solutions, including all digital data and technology ini­tiatives. He is renowned for his dedication to building highly technical and proficient teams that excel in meeting customer objectives, exemplifying his passion for driving organiza­tional change.

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