The Insurance Industry

The insurance industry is notorious for having been a slow adopter of advancing technology in the past. In recent years, however, advancing technology and the sheer amount of data required in claims processing and other processes have force the sector to adopt the use of AI to stay stay ahead of the competition. A recent study from Tata Consultancy Services reported that the insurance sector has invested over $124 million in AI, trumping the $70 million invested in other industries.

Nevertheless, insurers face a number of challenges in their quest to claim and retain customers. Large-scale mistrust, fierce competition and an increasingly demanding consumer base are forcing the industry to evolve quicker than ever. If they want to survive, they are turning to AI to help streamline their workflows, decrease fraud and increase customer-centricity.

The insurance industry is facing more challenges than ever

Consumer expectations are changing. Millenials are demanding a more hassle-free and customer-centric shopping experience.

In a sector suffering slow growth and fierce competition, firms are being forced to work smarter.

Lack of trust burdens the industry, causing many to view insurance as an unnecessary expense.

Product overview

The claims management process is time-consuming and data-intensive including various types of documents such as claims forms, incident forms, medical reports and repair estimates, all with data in both structured and unstructured formats. This data is essential to evaluate the validity of a claim, to ensure against fraud, and to route it through all of the decision making processes.

Our claims management system aids insurers and underwriters speed up the process of evaluation by extracting essential data in all types of documents and structuring it to be fed into the database automatically. This drastically reduces time spent on data-checking, allowing insurers to spend more time focusing on customer-centricity efforts.

The claims management system is a modular solution that allows you to leverage AI capabilities in where needed, from a single step in the mortgage assessment process to across the entire workflow.

Data extraction

Our extraction AI models can be customized to know what to look for in incident forms, medical reports, repair estimates, etc.

Claims processing

Classification models classify claims documents rapidly, cross-checks data for errors and identifies missing or incomplete documentation.

Fraud detection

AI models can learn and discover new cases in new scenarios, automatically evaluating damages and predicting the costs from historical data to prevent fraud.

Success story

Reducing costs, saving time, and increasing accuracy in the life insurance claiming process to improve customer centricity


A life insurance company offers an annuity-based product in which upon the death of the insured, relatives are able to make their claims by supplying the death certificate and personal identification through the post or email. These documents are scanned and the processing team enters the details manually into the system. The printed forms are then posted to the relatives who sign and send the forms back for the claimed amount to be released.

The process is time-consuming, labour-intensive and potentially riddled with mistakes made during the data-entry process, resulting in the delay of the release of funds. This process not only results in a loss of time, resources and costs for the insurer, but adds additional emotional burden on the relatives during an already emotionally difficult time.


This problem is well-suited to the capabilities of Nexus FrontierTech’s bespoke AI solutions services, in this case a tailor-made Cognitive Document Reader (CDR). Whether the relatives send the death certificate and personal ID in the form of scanned versions or photocopies by post, the CDR can extract all the required information and place it in the database automatically. The processing team would simply double-check the details before releasing the claim.

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