Context

Using the old ways of the insurance claiming process, there had been some negative feedback associated with the long duration, low responsiveness in case of customer interaction, and also lack of transparency of the process. Traditionally, insurance claim forms had to go through at least three steps to be fully processed and approved: submission through an online portal, completion with additional required documents sent manually through the post, and finally review and verification – before the process could continue to the damage assessment process. These steps would normally require days or weeks to be completed, and the highly manual process made it difficult to provide transparency to the process that customers needed.

Their objectives :

To transform claim form processing, the company realized it had to simplify and reduce the steps and eliminate the paperwork.

The company understood that artificial intelligence models could enable itself to automate the process with natural language processing (NLP) to provide a quick claim filing process in real-time. Using NLP would allow automatic documents classification and customer identification by extracting phrases be it from text or native PDFs, hence eliminating the need for manual document sending and review by human officers. This way, the company would be able to make the claim filing procedure to be much quicker. Additionally, it would also save FTEs costs as using AI would eliminate the need for human officers to manually review the claim filings.

I knew that the steps and the overall process could be simplified, quicker, and simpler. Customers demand speed and transparency, on the status of their documents – but we couldn’t provide it.

Thomas

Chief of Operating Officer

Building an artificial intelligence model to automate a previously highly manual insurance claim filing process was not as simple as the company thought. Firstly, the challenge was to develop a classification scheme and solid strategy so that the NLP model could well-understand and classify the type of documents, client information, and also types of claims in the filings. The mapping of potential misclassifications also had to be done correctly.

Secondly, while the need to train the AI model exhaustively with clean labeled datasets was understood by the insurance company, it was quite difficult to find publicly available datasets that perfectly matched the company's needs, as claim filing usually involved sensitive data. Hence the challenge to build suitable high-quality training datasets for the AI model arose. Furthermore, another challenge was to allow human feedback on data correction and reclassification in the AI model when a mistake in identifying information, document, or type of claim is spotted. A human in the loop was important to enrich and solidify the model.