Intelligent document processing (IDP) is a form of artificial intelligence technology that employs Deep Learning technology to automate processes that involve unstructured data. It’s a big step in the evolution of AI and intelligent automation technology. Previous generations relied on the use of keywords, enterprise search, and highly detailed rule based systems to try to automate processes involving unstructured data.
Such process automation solutions face two big limitations. First, they work only if the rules accurately capture the problem to be solved, similar to robotic process automation. If any element involved in the process strays outside the predefined rules, the automation breaks down. Second, it’s an expensive approach, requiring the business hire full-time information architects to build and maintain the rules or hire a third party to do it for them.
With Deep Learning, users only need to provide examples of what they’re trying to achieve and algorithms create models to achieve that goal. With IPA solutions, you can apply deep learning to processes that normally require some form of human decision-making or input.
IPA solutions help insurance companies get more out of their existing employees, effectively expanding their capacity. It also enables faster and more accurate decision-making, leading to improved cycle times and customer service. What’s more, IPA improves process efficiency by reducing the number of touch points, thus freeing up employees to focus on other, higher-value activities.
Following are three examples that demonstrate the value of AI and process automation technology in the insurance industry.
Automated Claims Processing with IDP
When an insurance client submits a claim, it typically comes with multiple types of information, including text-based documents that describe the issue and images showing the damage. Most if not all of the data is unstructured.
With IPA, insurance firms can automate the process of extracting relevant unstructured data from a claim submission, both from industry standard ACORD forms and non-ACORD forms. It can be used to automate the claim classification process, for example, to more quickly get the claim to the most appropriate adjuster. It can also aid in claim annotation, to help the adjuster understand the basis of the claim, and in identifying potentially fraudulent claims.
Applying IPA to Regulatory Compliance
Insurance is a highly regulated industry, with various requirements state by state. That makes responding to state requests for information an important business process and a time-consuming one.
With IPA, insurers can develop natural language queries that enable them to automate processes such as the retrieval of a provision or a term definition and relevant explanations of variability (EOVs) for a specific state. It can also be used to compare provisions or term definitions across states, showing language that’s been added or removed.
Insurers can also use IPA to automate the process of finding historical responses to similar questions and objections to similar responses.
Automating Commercial Underwriting Processes
For commercial insurers, the underwriting process can involve thousands of pages of documentation that have to be reviewed in order to come up with an accurate quote. Insurers can streamline the process with IPA by automating the process of data extraction to improve overall workflows for internal stakeholders.
Such a process enables the company to automatically extract the relevant unstructured data from forms related to various specialty insurance products applications far more quickly, resulting in reduce response time to customers, thus improving customer satisfaction. For the insurer, the process also improves accuracy, efficiency and profit.
IPA Ushers in a New Era for Insurance
To learn more about how automation and artificial intelligence technologies are bringing all sorts of new capabilities to the insurance industry, download our Insurance Industry Automation Guide here.
