AI and intelligent document processing for the insurance services industry

March 2021 | EXPERT BRIEFING | SECTOR ANALYSIS

financierworldwide.com

While artificial intelligence (AI) has gained traction with enterprises in other industries, insurance providers have been slower to adopt. In 2016, research by Deloitte noted that less than 2 percent of insurance companies were currently investing in AI technologies. This same research noted that overall funding in AI development was projected to reach $47bn by 2020.

Today, as AI has become prevalent, more and more insurance companies are looking for advanced solutions that will help them effectively process their repositories of day-to-day paperwork and the unstructured data they contain. Intelligent document processing (IDP) platform solutions that leverage AI technology are uniquely positioned to help these companies realise significant gains.

The unstructured data challenge

Many insurance companies face similar challenges when it comes to handling paperwork. The unstructured nature of the data, and the sheer volume of content, make it tedious and time-consuming to process manually. Mistakes, whether human or as the result of legacy solutions that are not designed for the task, can be frequent and costly.

IDP is a method of automating data extraction and processing that combines AI techniques, like natural language understanding (NLU) – an advanced form of AI capable of deriving meaning from entire sentences and provisions – with tools like optical character recognition, to create a single platform that processes unstructured data.

For any company dealing with large quantities of unstructured data, finding an effective IDP solution is a great opportunity to improve process efficiencies, reduce costs and increase margins.

Three applications of IDP for the insurance industry

When preparing new quotes, underwriting teams are typically tasked with reviewing and extracting key information from prior carrier plans and submissions in addition to comparing locally issued policies against their company-issued counterparts. Over the course of a year, the process can involve thousands of documents and is onerous work. One company we spoke to, a commercial property insurance company, stated that their review team spends fully one-third of their time simply comparing documents and looking for differences in provisions. IDP-driven automation can significantly reduce the time it takes to review and compare these documents while increasing the accuracy of data extraction.

To be effective, an IDP solution should be able to accurately identify and extract key information found in terms and provisions, interpret and classify extracted information, compare text and, based on meaning, make it easy to search, locate and redline differences in provisions.

Many IDP solutions are based on a mainstream AI approach that combines machine learning (ML) and statistical models. They might improve on what manual labour can achieve, but these types of solutions require significant training before they can be optimised for large-scale deployments, a factor that can leave a project stalled in training purgatory while efficiency gains remain out of reach. Even after lengthy training, ML models may still be unable to approach the accuracy of an IDP platform that utilises advanced NLU to provide context-based meaning.

Submissions workflow

Large insurance companies often deal with extremely high volumes of email submissions that come in through their regional field offices. These emails contain prospect information and insurance census data (essential information about individual persons who will be insured) that must be reviewed, sorted and then forwarded to the appropriate prospect ‘buckets’ in the company’s customer relationship management (CRM) portal, where quote specialists will review the data and use it to prepare insurance quotes.

By nature, the process of reviewing, extracting and routing all this data is cumbersome, given the sheer number of different products, brokers and client details that must be correlated not just from the email content, but from information contained in attachments. Adding to the difficulty is the fact that email submissions often do not adhere to a single format. Some may use an industry-standard template while others may organise data in a company-approved structure, making it more time-consuming to consistently and quickly locate key details without error.

An IDP platform solution can significantly improve the accuracy and efficiency of this submissions process by using AI to automate the processing of email content and attachments to extract targeted data, classify and categorise the extracted data based on the identified product, prospect and type of business, and route the extracted data into the CRM.

Using IDP to cut intake turnaround time from hours to seconds will allow companies to meet market demand more quickly.

Loss run reporting

For insurance companies, loss run reports act as credit scores that provide loss experience and claims history reporting on potential new customers. Typically, an insurance company will request at least five years of coverage history on a prospective customer and use the loss run data it receives to assess potential risk when creating policies and respective pricing. Reviewing these reports, which may be delivered in a variety of formats, and copying the relevant information they contain into a company’s underwriting system is manual work that takes time and may lead to errors that can negatively impact pricing accuracy.

Automating this process is a great way to reduce costs while boosting efficiency and increasing underwriting confidence in the accuracy of loss run data. An IDP platform that leverages a meaning-based AI approach will be able to provide significant gains in speed and accuracy for these types of projects. Approaches that establish meaning through context typically require fewer training documents to create viable models and can move a solution more quickly into production. Other capabilities, like the ability to work reliably with a broad range of report formats and accurately extract information from complex tables, also help ensure that an IDP solution can be effective.

These three examples represent just a sample of the value that AI and IDP can bring to the insurance industry. Meaning-based NLU approach coupled with document processing will enable high levels of accuracy while automating and expediting lengthy, labour-intensive workflows that are typical for the industry.

Francisco Webber is co-founder and chief executive of Cortical.io. He can be contacted on +1 (888) 933 6658or by email: f.webber@cortical.io.

Francisco Webber

Cortical.io