Insurance claim processing is a complex, yet fundamental piece of the foundational infrastructure of an insurance organisation. To put it simply, a customer submits an insurance claim when an insured asset acquires damage that is covered in the customer’s insurance policy. The client submits all relevant contextual information that will help the insurance company determine if the claim will be fulfilled. As straightforward as this process may seem, it becomes extremely difficult to keep up when the average insurance company receives thousands of claims every day. Beyond that, the information required to analyse and determine the outcome of a claim has increased; making it more complex and labour intensive.
In insurance claim automation, organisations have to establish a balance of automation to speed up the process while still handling the customer with care to build trust and retention. On one end, the customer wants quick and painless help that is readily available. On the other end, the insurer is seeking efficient processing that is error-free while also ensuring there is no fraudulent activity and a positive customer experience.
As mentioned earlier, when submitting a claim today, the customer has to provide more information than ever before. This has created an influx of data and documents in an already document-heavy process. While many customers are submitting claims online, there is still an overwhelming volume of unstructured documents riddled with poor handwritten data that makes it difficult to extract the relevant information and match it to the respective insurance policy efficiently.
Our Data Extraction translates all unstructured data such as handwritten medical reports, motor claim forms and prescriptions into structured data. Once data extraction is complete, the data will be matched against the policy document using Deep Learning and Natural Language Processing. Intelligent data extraction allows insurance companies to move through customer data efficiently to deliver results quickly and accurately.
Natural Language Processing and Policy Check
One of Unitek's NLP applications is contextual reasoning through using NLP and Knowledge Graphs. NLP is used to understand the contents of the policy document and enables us to build the rules within the policy to automatically check whether the claim is valid in regard to the policy agreement.
Unitek leverages all unstructured data from a claim to acquire more information using external providers. We enrich individual claims with real-time information sourced from web-based and public data sources. All sources of data are acquired from GDPR compliant data providers therefore there is no risk of a data breach. Simultaneously checking for fraud, abuse and outliers referring to both macro and micro level data sources.