Digitalization in underwriting
Complex topics, high-tech support: AI technology does not replace an underwriter, but it can assist in the daily work with long, complex texts and massively accelerate processes.
Complex and anything but trivial
Reviewing insurance applications and weighing risks - the insurance contract should cover as many risks as possible for customers and stand up to competitive comparison, while the risk must be viable for the (re)insurer. It’s about winning customers, retaining customers and profitability.
Underwriters have a demanding task. Complex issues must be subjected to a robust risk assessment. For this purpose, various, often long documents, files or records are analyzed. The texts and data to be examined are rarely available in standardized formats. This means that data cannot simply be made accessible using standard text recognition methods (OCR). So how can the processes be automated?
AI technology makes the analysis of complex texts possible
Natural Language Processing (NLP) with Deep Learning makes it possible to quickly and precisely extract data and core information from texts and documents - regardless of their form, structure or wording. For this purpose, the individual text modules are not only recognized, but also set in relation to each other.
In this way, not only basic information can be extracted from documents - potentially risky clauses can also be highlighted.
Support of daily work and process acceleration
The technology for analyzing documents and text can support the underwriting process and massively accelerate time-intensive basic activities in particular.
Whether it’s a doctor’s letter or pre-formulated contracts from industrial brokers, underwriters receive important information quickly and accurately. In addition to dates and deadlines, this also includes important information for risk assessment, such as coverage commitments including coverage amounts and sub-coverage amounts or critical clauses.
With AI technology, the specific features relevant to the industry or the insured event can be extracted from large volumes of text and made available in a structured form for viewing, further processing and evaluation.
This not only saves underwriters themselves a lot of time and ensures even greater precision, but of course also benefits insurance companies. Important information is available quickly and risk assessment can be carried out in an even more structured manner, which in turn increases profitability. Last but not least, customer satisfaction also increases - because customers naturally receive an offer more quickly.
The contract portfolio as a source of information
Natural Language Processing (NLP) with Deep Learning makes it possible to quickly and precisely extract data and core information from texts and documents - regardless of their form, structure or wording. For this purpose, the individual text modules are not only recognized, but also set in relation to each other.
In this way, not only basic information can be extracted from documents - potentially risky clauses can also be highlighted.
The right approach
Not all insurance companies work in the same way and a wide variety of IT systems are in use. The right step for the successful use of AI-based document analysis for process acceleration is therefore the one to the expert - ideally together with the in-house IT specialists. Together, the best individual solution for the really relevant areas of application can be found and implemented.
Your contact
As a specialist in Intelligent Document Processing in practical use, kinisto will be happy to advise you on the subject and on your specific case!
