A machine learning-powered system for extracting information from insurance claims. Table recognition, processing forms of different layouts and designs, detection of input field types, and data extraction.
Our client is an insurance agency working with hundreds of client-submitted claims and forms daily. The process of manually extracting relevant information from the documents is time-consuming and highly error-prone, therefore our client decided to implement an automatic document processing system to help them reduce the workload and improve data extraction quality.
The main challenges we faced were a high variety of claim templates and designs as the layout and design of an insurance claim can look different depending on the claim type (medical insurance, business insurance, life insurance, etc.) and type of client (individuals, businesses, corporations), as well as extraction of data from complex tables.
We have created an AI-powered data extraction app for insurance claims that detects document structure and extracts relevant data in a matter of minutes. Our app extracts data from the claims and prepares it for further processing.
The app detects claim type (medical, business, etc.) and its layout to improve data extraction and its accuracy. It extracts claim type and title, automating the document identification and digitization efforts. The app also supports batch processing, meaning it can process multiple claims at once.
Our app can work with various claim templates and designs, allowing for seamless data extraction from different claim types. The app can detect input fields and determine their type, like free-form text boxes, time and date fields, and more, as well as extract the data from the fields.
The app can work with claims of different designs, including complex formats that span across multiple pages, multiple claim forms on one page, input fields of irregular size and form. It not only extracts the input data, but also data type (text, date, number) and the name of the input field along with its type.
Insurance claims have many different data field types, all of which look different from each other and present relevant data differently, for example, in a form of plain text, a checkbox, date and time, etc. Our app can detect multiple input field types, and extract data accordingly, for instance, the app can detect checkboxes to determine which of the multiple options was marked.
Claims our client works with often include tables with important data that needs to be extracted and processed. The app we have developed can analyze tables of any complexity, detect table cells, extract table title and its content into an Excel file for further editing.
Our client is currently using the app to process hundreds of insurance claims every week, improving their processes and reducing data processing errors.