Intelligent Document Processing for Insurance

Many insurance companies face similar challenges when it comes to handling paperwork, and errors in legacy solutions that are not designed for this task can be frequent and costly. This makes manual processing cumbersome and time-consuming, and can cause errors that can cost you millions of dollars.

For companies dealing with large volumes of unstructured data, finding an effective IDP solution can be key to improving process efficiency, reducing costs and increasing margins. IDsP is a method of automated data extraction and processing that combines advanced forms of AI that are able to derive meaning from whole sentences and rules to create a single platform for processing unstructured data

The challenges that insurers face are REAL.

We often deal with large insurance companies and e-mail submissions come from their regional offices. These emails contain information about insured persons that must be checked, sorted and then forwarded to the company's Customer Relations Management (CRM), where the quotation specialists view the data and use it to create insurance quotes. The process of verifying, extracting and transmitting this data is, of course, cumbersome, as there is an enormous amount of data that all needs to be correlated, not only with each other, but also with the insurance company itself.

To make matters worse, email submissions in a single format are often not followed and industry-standard templates are used, while others organize the data in companies - approved structures - making it time-consuming to find important details consistently and quickly without errors. IDP's platform solution improves the accuracy and efficiency of the archiving process by automating the processing of email content and attachments to extract targeted data, categorize the extracted data based on the identified products, and forward it to the CRM.

Here is what you NEED.

The insurance industry processes specific types of documents through the customer lifecycle. The extracted information is used in making important, timely decisions in processes such as underwriting and adjudication. As a result, Roboextract’s features are built to allow for quicker adaptation in a given client environment.

Roboextract’s Natural Language Processing (NLP) and Machine Learning (ML) solution is purposely built for many industries including insurance. Roboextract helps commercial insurance brokers and carriers extract, interpret, classify, and analyze unstructured data in policies, applications, submissions 350 times faster than a human can