Exploring the Impact of AI on the Claims Process in Insurance
AI technologies are transforming the insurance industry. And they are doing so at a remarkable rate. Nowhere is this more apparent than in the claims process.
In this two-part blog series, we explore the impact of AI on the insurance claims process. In part one, we set the scene and establish the context in which AI implementation is occurring. We examine the critical customer frustrations AI technology can resolve, discuss the interplay between cognitive AI and generative AI (genAI) and look at how insurers can begin implementing these tools.
Customer Satisfaction is All-Important
Insurance companies will leverage AI technology to meet various aims, objectives and needs. For instance, in the current market, many insurers face cost pressures, which they hope to address by focusing primarily on the automation of key processes. AI can and will play a role in helping them achieve this.
However, a significant percentage of C-suite executives in the insurance sector recognize that AI will have a more extensive and substantial impact on customer experience. For these insurers, customer satisfaction – not cost savings – drives AI adoption in claims.
At the root of this is the recognition that AI technology can help eliminate several prominent customer frustrations.
Enabling Insurers to Overcome Customer Frustrations
The delay between receiving the First Notification of Loss (FNOL) and settling a claim is a key customer frustration across all insurance organizations. The highly regulated nature of the industry and the complexity of the claims process contribute to a situation in which claims settlements take far too long – often months.
When settlement takes such a long time, it erodes customer trust. Customers interpret delays as an unwillingness to settle or pay what is due. Consequently, a lengthy claim is not only perceived as negative experience, but rather as a relief as it was not rejected. It does not reflect well on the insurer – nor does it engender positive customer sentiment.
For customers, the process generates little satisfaction. Ultimately, customer satisfaction determines customer retention, reference rates and Net Promoter Scores.
Eliminating Uncertainty in the Claims Process
Uncertainty also tarnishes the customer experience. In current claims processes, customers are regularly frustrated by a lack of information. This contributes to a substandard customer experience and often complicates the claims process.
Absence of information regarding a claim encourages customers to act in ways that impede claims settlement and prolong the settlement period. Providing essential information and ensuring claimants are aware of claim requirements upon receipt of an FNOL eliminates much of this uncertainty.
Consider an insurer that utilizes AI technology to contact a claimant as quickly as possible to inform them they will receive an FNOL follow-up call within the next few hours and need photographic evidence of the damages they are claiming. This insurer makes the customer less likely to take actions that compromise the claims process. In this instance, those actions might include sending the damaged item away for repair without first taking the required photos.
In most cases, uncertainty is poison to the claims process and customer satisfaction. As we will see in the second part of this series, AI facilitates quick, informative and effective customer communication and plays a pivotal role in eliminating uncertainty.
Combining Cognitive AI and GenAI
Insurers have been leveraging cognitive AI for many years. So, it’s understandable that the new generation of AI technologies, most notably genAI and Large Language Models (LLMs), are lingering in the limelight and generating the most hype among insurance organizations.
Insurers need to think about AI holistically. In many cases, the real magic happens when genAI, LLMs and cognitive AI work together. Often, it is the interaction between the various technologies and applications that unlocks AI’s most potent capabilities and enables insurers to employ the technology most comprehensively.
Consequently, any considered AI strategy will concern itself with the interplay between cognitive AI, genAI and LLMs. It will also attempt to identify areas and processes where insurers can combine the tools to deliver maximum value.
Implementing AI & Identifying Suitable Use Cases
For many organizations, it’s difficult to keep pace with the rate of AI development and evolution. Decision-makers recognize AI will transform the industry and know that implementation must begin now. To hesitate is to fall behind. At the same time, insurance companies must contend with a lack of technical resources and AI expertise.
Fortunately, insurers do not have to design, develop and implement AI initiatives alone. The improvements and innovations we cover in this two-part series are available as Software-as-a-Service applications.
Insurers should select initial use cases according to project simplicity and ease of implementation. Complex insurance claims are not quick wins and challenges early on in AI adoption can disrupt and delay long-term progress. With this in mind, it is best to focus on insurance policies and claims that can be reduced to a precise decision map based on relatively few criteria. Demonstrate AI success with simple insurance policies and claims processes, then build from there.
Shaping the Claims of the Future
It’s obvious that AI will revolutionize claims management and processing in the insurance industry. Insurers should take part in these exciting changes.
In part two of this series, we will look at the four components of the claims process where AI will have the most significant impact and detail how insurers can deploy the technology to streamline claims management. Fill out the form to be notified when part two is live.
If you would like to learn more about the impact of gen AI across the insurance value chain, read our white paper here.
