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Improving productivity and efficiency in insurance claim form processing

Improving productivity and efficiency in insurance claim form processing
Industry
Insurance
Uses cases
Claim processing

Impact

Challenges

  • Build relevant training datasets adapted to the specific use case of the insurance company

  • Working with a human in the loop workflow to ensure the labeling quality and enrich the dataset

  • Scale rapidly the volume of data that the team can process

Solutions

  • Friendly UX supported seamless navigation in the data annotation process

  • Customizable UI to fit the specific needs of the various use cases

  • Intuitive and precise annotation tools making it easy to onboard third party labeling provider

  • Advanced collaboration and reviewing tools to provide human in the loop workflow, efficiently control the label quality, and identify how to enrich the datasets when necessary

Context

A global insurance company aimed to simplify the steps of processing insurance claim forms, at scale. The objective was to improve the productivity of the insurance claim filing and reduce the complexity of the steps involved in the claiming process.

When the company decided to adopt artificial intelligence at the heart of the transformation process, Kili Technology played its part in preparing clean, quality training datasets for AI to build and refine the AI model through data labeling.

How it all started

The importance to reduce the steps of claim filing with AI

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

Challenge

The challenges of building AI automation of claim filing

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.

Issues with complex document processing in Insurance

  • Multiple docs in a single PDF

  • Many possible variations: in the location of the data, between sources

  • Complex data : Complex layouts

  • Nested tables, Handwriting, Multiple docs in a single PDF, Symbols, Images, logos,

Solution

A versatile training data platform to implement Intelligent Document Processing (IDP)

Moving forward with the development of the AI model, the company understood its need to find an advanced and reliable end-to-end solution for data annotation to build a rich training dataset for the AI model training process, in order to solve the challenges the company was facing. Before acknowledging Kili, the company partnered with another provider, however as complexity progressed the tool couldn’t catch up with its expectations. Moreover, the insurance company struggled to use its initial tool when the human in the loop was involved, as it was not very suitable. Stopping the partnership, the company experimented with annotation tools and decided to pursue the project with Kili Technology.

The company found Kili’s interfaces to be easy to use and customizable to its needs, with an intuitive UX that supported seamless navigation in the data annotation process. Moreover, partnering with Kili enabled the company to extend its labeling capabilities with professionals trained in the project topic. Using reviewing features on Kili also made collaboration between the internal data scientists and these external professionals fluid and easy to manage. On top of that, Kili stood out among other tools as it was the most convenient tool to iterate with humans in the loop process for the correction and reclassifications.

It was very easy to iterate the labeling process to correct the work of our AI when it was wrong. We were able to import predictions by the AI very quickly to the labeling interface, and the features and the shortcuts within Kili allowed us to modify them easily.

Vincent
Artificial Intelligence Director

Impact

“Such a meaningful impact”

Transforming the insurance claim filing procedure with artificial intelligence has brought the company visible benefits in terms of productivity and efficiency. Leveraging NLP, the company has simplified the claim form processing to become a quick one-step procedure. Speed was one of the highlights of the result of the transformation, as the usual weeks of procedures became only 15 minutes – which was the time taken to fill out the comprehensive insurance claim form. Identity verification and claim reviews were done in real-time by the AI.

Aside from time-saving, the company also saved costs as the insurance claim filing no longer needed a human workforce in verifying the customers, the documents, and the information included in the claim filing. The AI automation enabled the company to save costs for approximately 5 FTEs per year. The employee productivity improved as they were no longer involved in the tedious paperwork process and contributed to the meaningful AI training during the human-in-the-loop.

I mean, can you imagine? From weeks to only minutes to process the claim form filed by customers. We are so much more productive, such a meaningful impact.

Thomas
Chief Operating Officer

Lesson Learned

  • Transforming insurance customer insurance claim filing process through artificial intelligence could drastically speed up and simplify the procedure, resulting in higher productivity.

  • High-quality AI performance to classify files and recognize information in insurance claim form processing plays a vital role in a fast claim file review. Data labeling then is crucial to train the AI model to achieve this performance.

  • A powerful end-to-end annotation solution to build training datasets for classification and entity recognition for insurance claim files is important to improve efficiency and productivity for time and cost-saving.

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