The difference between content and data, and why it matters
“Context is just as important as the data itself when making decisions in insurance,” said Twigg. “Most solutions are data-centric, which reduces content to a data-entry problem after the customer call is finished, rather than a rich source of insightful contextual information needed to make decisions in customer journeys. Even if back-end systems get the data they need, the insurance case worker still has to review, assess and make judgments from documents received as evidence in the case, bringing the worker’s experience and expertise into its adjudication – a process that cannot work on data extraction alone.”
These informed decisions can revolve around underwriting, policy coverage or claims. While there are process and workflow systems that help automate the process of adjudication, these solutions work off data to help make the flow of tasks easier. But decision-making still requires an understanding of all the rich content.
“Current processes aren’t designed for adjudicating content. Traditional systems of record like business process management, case management or custom applications run on data, and content is relegated to the role of data source to feed these systems when it should be the basis for expert decision-making,” added Twigg.
In other words, content is no longer just data to be extracted. It’s important information that can be interpreted and understood using modern AI and ML to provide quick insight and improve the decision-making process that enhances the customer experience.
There’s also the sheer amount of information to consider and the challenge of sorting it. If the industry runs on content, shouldn’t intelligent processing that content be a central part of the solution? In this highly-competitive industry, where customer loyalty must be regained through every touch, shouldn’t the exchange and adjudication of information be seamless?
“Modern AI and machine learning can understand the richness of content and still do the data extraction and validation where needed,” he said. “The goal is to recognize and process the content as rich information, rather than just a source of data.”
Using content to its highest potential also aids in fraud detection throughout the process. Fraud can be detected through recognizing suspicious information in documents, something that can be overlooked when just focusing on data extraction. A form of AI, natural language processing (NLP) can recognize patterns of text and spot suspicious communication to be flagged for further investigation. Coupled with careful discovery of how processes are working, insurers can create user experiences that delight customers while adding further protections against fraud.
Twigg says adding AI to document processing yields a huge benefit to brokers and claims adjusters. They will be able to make decisions more effectively and efficiently by recognizing, tracking, and alerting those patterns of communication.
Furthermore, with the insurance experience centered in the palm of your hand – especially now when we seek to remove contact from interactions – smart phone capabilities have become increasingly important. Twigg says it all leads back to an improved, seamless customer service experience. Today’s insurance clients are market and tech-savvy and understand their choices, so insurance companies must differentiate on the efficiency and effectiveness with which they engage customers and handle their information.
“Processing content should not be a hurdle in your interactions with clients; it should be fluid and instantaneous,” Twigg said. “It’s not enough to automate an existing process anymore. We need to redesign them so content plays a central role throughout the process and is not just a data source.”
Register here to join Reginald Twigg for the webinar, Getting Digital Insurance Right Starts with Content on Wednesday November 11 at 10am PST.