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Exploring the Impact of AI on the Claims Process in Insurance Part Two

Exploring the Impact of AI on the Claims Process in Insurance Part Two

In part one of our series on the impact of AI on the insurance claims process, we looked at why AI innovation is necessary and how it improves claims management by helping to overcome customer frustrations.

In part two, we will expand on these ideas and apply them to four distinct areas within claims, providing concrete examples of how AI can improve insurers’ abilities to streamline processes, empower claims adjusters and deliver satisfying customer experiences.

1. Collection of Claims Information

On receipt of a First Notification of Loss (FNOL), insurers begin collecting the information required to process a claim. At this stage, effective customer engagement, interaction and communication are crucial. One of the principal ways AI will improve these processes is by making automated FNOL channels more empathic. There is a common misconception that automated channels, such as chatbots, are inferior to human agents because of their lack of empathy. Instead, automated channels can offer even more empathetic, customer-centered experiences.

It is important that insurers address the needs of their customers who have just submitted a claim empathetically as it could be emotionally challenging. However, only limited resources are available for this human-approach. Chatbots have been leveraged in the past but discarded due to the excessive rigidity and cold tone, so an alternative must be ideated. That’s where generative AI (GenAI) comes into place.  – Antonio Di Marzo, Senior Director, Product Development, EPAM

Empathetic automation tools acknowledge and respect the situation in which claimants find themselves. FNOLs are typically made moments after an incident and claimants are often in a charged emotional state. They are likely stressed and can struggle to recall information or perform basic tasks.

In this context, asking complex questions and making challenging demands frustrates the customer. It can be a slow and inaccurate means of retrieving information. AI technology can tailor early claims interactions to these exceptional circumstances by making communication easier and simplifying the information required to progress.

Case Study: Easy, Empathetic Customer Interactions

EPAM’s Embedded Gen AI solution, enabled by large language models (LLMs), demonstrates this expertly. Cross-referencing available information, it verifies caller identity quickly and efficiently using everyday data, such as date of birth and the caller’s phone number. Rather than identifying, say, a vehicle using difficult data points, it refers to more immediately available information, such as the car’s color and manufacturer.

“Are you claiming on the blue Mercedes or the red Audi?” replaces “Can you provide us with the vehicle’s date of registration?”

In this context, AI helps deliver a more natural, and less frustrating, customer experience. At the same time, it streamlines the initial collection of claims information, making it easier on claims adjusters as well.

2. Categorization and Reconciliation of Claims Information

The next stage is categorization and reconciliation. Insurers need to understand the type of damage being claimed, retrieve the relevant insurance policy and identify which elements from the claim they need to compare with the policy details and reconcile that data.

Cognitive AI and Gen AI assist in this aspect of the claims process by automating the identification of relevant details and cross-checking and validating them. At any point in the reconciliation process, an AI-enabled solution can escalate a case to a human adjuster if certain conditions are met, such as irretrievable information, case complexities or suggestion of fraud. This guarantees human oversight of the process and ensures the case is passed to the adjuster in a more mature state than it otherwise would be, with the maximum amount of necessary information collected.

Automating this aspect of the claims process with AI technology also enables insurers to move quickly to reassure customers, provide them with information and eliminate uncertainty. Within moments, insurers can confirm whether their policy covers the damage, whether there is any excess they’ll need to pay and if they require additional information to progress.

3. Facilitating the Work of Claims Adjusters

AI will further improve claims processing and settlement by equipping claims adjusters with a smart workbench. When the initial claims automation process escalates a claim to an adjuster, the smart workbench helps them resolve the issues and inconsistencies quickly and efficiently.

Historically, claims adjusters often worked with five or six different systems, jumping between them to identify and retrieve data and process a claim. Adjusters had to problem solve the case, navigating complex and lengthy documents and multiple platforms in search of relevant information.

Today, AI and smart workbenches assist adjusters by identifying missing and problematic information, advising on where to find it or how to retrieve it and providing clear instructions for next steps. They leverage API integrations to interact with various systems and enable easy data extraction. Ultimately, they make claims adjusters’ workflows more efficient and streamlined, improving processing times.

Additionally, AI will have a significant impact on fraud identification. While insurers utilize natural language to detect fraud in the textual aspects of FNOLs, visual AI systems can help identify fraudulent images that are manipulated, edited or sourced from the public domain. However, fraud identification is a double-edged risk. Wrongly identify a legitimate claim as fraud, and the insurer may lose a customer. Fail to identify fraudulent claims, and the insurer pays for non-damages. Consequently, AI tools must prioritize precision and accurate detection over an over-cautious, over-zealous approach.

As large-scale fraud grows more sophisticated and criminal organizations leverage AI to make fraudulent claims, insurers must respond by equipping adjusters with increasingly capable AI detection tools. In some senses, insurers are engaged in an AI arms race with criminal organizations. It’s a race that they cannot afford to lose.

4.Claims Settlement and Prevention

“In recent years, insurers have leaned towards automation or full direct payments with very little processing. A rapid disbursement narrative has developed with many insurers boasting about their quick pay-out speed. However, are insurers really serving their customers by only paying in the fastest way possible? The answer is no – consumers not only want their claims processed swiftly but also seek a higher level of customer care and a better experience overall.” – Antonio Di Marzo, Senior Director, Product Development, EPAM

Claims settlement benefits enormously from AI implementation. While automation will speed up the settlement process for straightforward claims – even making instant settlement a distinct possibility in some cases – it will also streamline interactions between insurers, customers and third parties. The customer experience will improve considerably with the automation of appointment booking for repairs, payment processing and similar post-settlement arrangements.

Additionally, AI will contribute to improved claims prevention services. This reflects many insurers’ desires to intervene in the customer lifecycle and help prevent claims, rather than simply reactively processing them. API-enabled connections with a wide range of valuable data sources allow automated claims systems to provide prevention advice in real time. For instance, EPAM’s Ico: Smart Insurance Claims system utilizes data from weather services to inform customers of potentially hazardous weather events and provide loss prevention guidance.

This approach is not limited to personal insurance policies. It is also relevant in the commercial sector. The shipping industry is an illustrative example. Automated systems enable insurers to suggest shipping routes based on available weather data and other relevant risks. In some instances, they may suggest a longer route that avoids an incoming weather event, reducing the insurance premium and fundamentally altering the nature of the risk.

A Bold Claim about the Power of AI

Over the coming months and years, AI technology will be the disruptive force in the insurance industry. Insurers’ ability to implement AI solutions will determine their ability to compete with rivals and provide customers with the services they expect and demand.

As this blog series has demonstrated, AI can be implemented throughout the claims process, creating better-connected and more efficient digital claims systems that reduce costs and improve the customer experience.

To learn more about the impact of Gen AI across the insurance value chain, read our white paper here.

If you missed part one of this series, you can read it here.


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