NLP: Artificial Intelligence in the Insurance Industry


Process long, complex texts automatically: From claims management and accelerating processes in customer service to contract management and premium calculation – automation using machine learning in the insurance industry is the key to greater efficiency, high customer satisfaction and competitiveness.

AI in Insurance: Natural Language Processing (NLP)

Latest technology enables automation of complex processes

Artificial intelligence is a core component of the digital transformation in the insurance industry – NLP is one of the most important technologies in use. Natural Language Processing (NLP) allows human language to be understood and processed. The AI recognizes information in context and reads like a human, only faster and more precisely. Even complex contexts are recognized – regardless of the structure of the document, the format and the exact wording in the text.

Processing of complex, long documents
(Almost) any format can be read
Ready to use in a few weeks
Seamlessly connected / integrated
Insurance and AI – in practical use

Inquiry Management

Fast processing and risk assessment
Classify inquiries and route them specifically to the right follow-up processes: Particularly with the large number of inquiries received by an insurance company, AI technology can not only provide support, but also offer very significant added value. Automated assignment significantly reduces the manual effort required for processing, enables better prioritization, and allows requests to be processed much faster. At the same time, the targeted extraction of insurance-relevant information makes it possible to automate risk assessment (PKV, BU, etc.) in parts. The AI extracts information regardless of format and wording – for example, from doctors’ letters.

Contract Management / Contract Analysis

Intelligent processing and analysis of complex contracts
Incoming contracts or draft contracts can be checked for important information in a structured manner using AI technology. In addition to basic data such as insurance partners, dates and deadlines, the AI can extract information specifically relevant to insurance companies – for example, on coverage commitments including coverage amounts and sub-coverage amounts. This makes it possible to quickly check even applications pre-formulated by brokers for potential risks.

Risks in the contract portfolio

AI-supported risk assessment
To manually review large amounts of text in very many, differently worded contracts would require an incredible amount of effort and manpower. AI used in the insurance industry can quickly analyze the entire contract portfolio. For example, coverage modules and sublimits in the contract portfolio can be read out. This is a valuable information base for strategic business decisions, because the exposed risk can thus be quantified at sub-coverage level.

Claims Management / Claims Settlement

Automated evaluation of damage reports

Damage reports reach insurance companies in large numbers. Reading and evaluating them is still a time-consuming process. The damage reports have to be analyzed to decide whether the damage is covered and how high the settlement amount will be. AI in insurance can support here - the processes can be partially automated. The AI extracts relevant passages from the damage reports and makes them available in summarized form as data records. This enables rapid further processing. This includes the automated determination of settlement amounts. Insurers can thus automate the reimbursement process (including comparison with award criteria and coverage amounts). This means fast customer feedback and settlement – at lower cost.

The implementation of a “traffic light system” that marks critical passages for employees can also already greatly facilitate and accelerate processing.

Customer Service

Fast response times and automated processing
The use of AI technology in the customer service of insurance companies revolutionizes processes and significantly increases customer satisfaction. The automated categorization and assignment of inquiries enables structured processes and reduces the duplication of processing. Even complete automation of routine processes is possible. For example, the dark processing of change requests to data such as IBAN or address.

Profitability measurement

Matching coverages
AI technology enables insurance companies to read data from large volumes of documents and text in a structured way. By analyzing claims reports and linking the data to contractually guaranteed coverages, it is possible to quantify the profitability of individual coverage modules. As a basis for business decisions, such information is a decisive advantage.
Advantages of using AI in the insurance industry


Processes previously only feasible through the use of (qualified) personnel can be automated through the use of AI in insurance.

Efficiency increase

By fully or partially automating work steps in a wide variety of areas using Natural Language Processing based on Deep Learning, processes become more efficient, the error rate decreases, and profitability increases.

Cost savings

Automation of processes saves costs for personnel and materials. The much lower error rate reduces the associated costs (time spent, lost customers or actual amounts of money).

Quality improvement

More precise, accurate and faster availability of information in a structured, usable form increases the possible processing quality and significantly reduces the error rate.

Customer satisfaction

The ability to handle processes and inquiries quickly and in a structured manner is the basis for smooth customer communication and good customer service.


As a core component of digital transformation, the use of machine learning in insurance makes companies fit for the future.
Voices from the industry / Case Study

AI-based analysis of industrial insurance contracts

Read the latest case study on the use of AI in the insurance industry
Software: Artificial Intelligence for Insurance

The AI system for processing complex documents

Efficient process automation: AI for insurance companies

With kinisto, information from documents of any length and complexity can be converted into structured data and quickly processed further - regardless of the input channel, formats or document structure. kinisto goes a big step further than conventional systems for automated document processing. Based on Natural Language Processing (NLP) with Deep Learning methods, kinisto recognizes information in context and makes it usable. kinisto is ready for use in the shortest possible time - from the first proof of concept to the deployment of the finished solution. As a specialist for Natural Language Process in practical use, we are happy to advise you!

The following is the basic process for using kinisto: