Artificial Intelligence (AI) is speedily disrupting the insurance industry. It’s advancing and revamping how insurers can deliver when it comes to claims services. The entire claims ecosystem, consisting of third-party administrators and carriers, with time is coming to understand the relevance of AI when it comes to reimagining the entire customer experience.
There are different types of AI – natural language understanding (NLU), computational linguistics, conversational AI, and neural networks, in their different levels of maturity.
AI technologies are often used in the claims process, to trigger alerts. It is useful in:
- Fraud investigations
- Harnessing data from sources
- External industry data
- Social media activity
- Credit bureau records
AI helps to combine domain expertise, efficient and specialised technology tools for an adept credit underwriting process.
Loss of accuracy and freeing up loss-adjusting resources for focusing on complex losses:
- Claim set-up aides bring together cognitive and NLU capabilities for processing unstructured content from the emails at the first notice of loss (FNOL) intake
- Chatbots that make use of conversational AI when it comes to customer service
- AI models can estimate the probability and extent of loss severity and estimation
As more and more insurers begin their AI journey, there are three key issues for planning a successful pilot:
Mapping the customer journey
Chronicling the end-to-end customer journey becomes critical, from both the carrier as well as the insured perspective. Using AI for loss inspection or claims set-up is important in the context of the claims journey for ensuring it's connected with other processes and ensures the customer gets a seamless experience.
Humans are the key to AI success
Operational alignment is essential in the entire claims value chain. As AI advances, claims professionals with reasoning skills as well as domain expertise will play a very important role in ensuring its success. Immature AI engines might often have to rely on these staff when it comes to handling exception paths, failures, and nurture the system for meeting the potential, depending on the context of the individual business problem that it's trying to solve.
Gearing up for the data tsunami
AI depends on a formidable foundation, which requires building, maintaining the accuracy, and being developed slowly and steadily extensively over a time period. This includes an assembling of data types, extending from text to media, estimate data, and loss of report that are crucial for computer vision.
But as the volume of data expands, so does the force to connect its insights. Combining the outputs of AI with other external and internal and data sets, for example, telematics devices helps to earn richer data and even improved insights that might be important for action. The systems that house data, as well as the necessary infrastructural needs, have to be completely customer-centric and accessible for handling the mounting myriad of communications across the lifecycle.
Getting started with AI
AI revolution comprises optimally bringing in the right building blocks for advanced levels of productivity and accuracy, a faster claims cycle, and at a fraction of the expenses – while delivering accurate decisions and greater customer experience. For insurers to thrive, they must plug the capability gaps and shape up a structural culture around AI.