Artificial Intelligence in insurance

Data Science and AI are transforming every facet of the insurance business – from underwriting and fraud detection down to marketing and customer service. Get to know the best practices and rules learned from our years of experience to set out on your own Data Journey.

Data & AI projects for the insurance industry

Few other industries possess a treasure trove of Data as vast as that which insurance companies are currently sitting on. This includes geographical, real estate and traffic Data in addition to client and damage-related Data. However, this Data is often only available in an unstructured, analog form. Text mining, Big Data Analytics and Artificial Intelligence (AI) can now be used to make this Data usable and to effectively interlink it.

The possible applications of AI in the insurance industry are massive and hold incredible potential.  With our years of experience in the financial industry, we can support you in identifying and selecting the right Use Cases as well as AI development.

A successful way to a digital future requires a holistic system.

That is why we have developed the Data Journey, which is more than just data science consulting. An end-to-end data strategy forms the basis and the framework for generating real added value from data. The goal is to test use cases as quickly as possible – from concept to prototype with real data. In our Data Factory, use cases are industrialized into finished products or services. In our DataOps, we operate and maintain your platforms and machine learning algorithms.

Customers Projects

The Data & AI experts at Alexander Thamm have already successfully implemented over 100 projects in the finance and insurance industry.

Automatic Damage Detection

Automatic Damage Detection

  • Claims settlement time reduced from 9 months to 10 days
  • 75 % reduction in experts’ fees
  • Cost savings in the millions combined with increased client satisfaction
Automated immediate settlement for small claims with NLP

Automated immediate settlement for small claims with NLP

  • Identification of Data points to enable the automatic settlement of minor claims
  • Reduction of settlement times from several days to just a few minutes
  • Know-How Transfer concerning the application of Machine Learning Methods to clients
Risk Assessment by means of NLP and Text Mining

Risk Assessment of organizations by means of NLP and Text Mining

  • Unstructured Data from experts’ reports are made analyzable
  • Faster, more efficient risk assessment
  • The “riskiness” of a company becomes quantitatively measurable
Application for insurance tariffs

Application for insurance tariffs

  • Reduction of risk issues by 78 %
  • Significant simplification of the application process
  • Automated evaluation of 1.300 characteristics from external providers
Data Analytics training concepts for insurance providers

Data Analytics training concepts for insurance providers

  • Employees are given a good understanding of Data Analytics and Data-driven Use Cases
  • The four developed training modules can be conducted and reused independently of each other
  • Training courses conducted in 5 separate countries
Roadmap Workshop for the insurance industry

Roadmap Workshop for the insurance industry

  • Generation and prioritization of close to 90 Use Case ideas by the 25 workshop participants
  • In-depth elaboration of the Top-3 Use Cases
  • Creation of a comprehensive Use Case library Evaluation of each Use Case according to potential benefits and feasibility

Munich Re Case Study

EN MR Case Study

Munich Re is one of the world’s leading reinsurers. Hardly any other company has more risk information than the Munich-based company. In order to collect this knowledge in a central location and enrich it with further data, Munich Re developed a data lake. In collaboration with the data science and AI consultancy Alexander Thamm GmbH, internal and external systems were connected via data pipelines.

Learn more in our free case study.

References

How artificial intelligence can be used

Precise risk assessment is a time-consuming and costly step when completing an insurance policy. That requires extremely detailed information, which previously needed to be collected by means of extensive questionnaires. In many cases this process has yet to be digitized. In order to improve efficiency in this area, it pays to separate customers into various categories. In the future, low-risk clients can be identified using predictive algorithms based on extensive profile and behavioral Data. These clients can then be offered a simplified risk assessment process. Such a measure would improve both the customer experience and company-internal processes.

In order to approach clients in a targeted and optimized manner, all relevant client groups must first be identified. Determining the relevant criteria for meaningful customer segmentation and categorization for the purposes of sales and marketing can often be quite a challenge.
In this case, so-called Unsupervised Machine Learning techniques, wherein an algorithm detects similarities in large Data sets without being given specific target values from the outside (as would be the case with demand forecasting) come in handy. This method uses a combination of inventory Data and external Data, in which similarities are recognized and grouped (clustered). The results of this process lead to effective customer segmentation that can be used to optimally address respective client groups.

 

Various studies have concluded that an average of 15-20% of all diagnoses are false. AI can help reduce the number of false diagnoses. Intelligent algorithms can compare millions of cases with one another in a few minutes or incorporate image and text databases into existing diagnoses, giving patients access to a second opinion with minimal extra effort. Second Medical Opinion grants enormous savings potential to insurance companies by reducing not only the number of incorrect treatments, but also the costs of the associated legal disputes and claims for damages through the use of Artificial Intelligence in insurance. 

Smart Homes in combination with AI open up completely new possibilities for property insurance – for example, home surveillance services can be bundled with residential building insurance Intelligent algorithms could detect unusual events by detecting anomalies in sensor Data that deviate from regular patterns. In addition, insurance companies would be able to offer their clients a mobile app informing them about potential risk of damage, such as certain weather events or a stove being left on. Individual supplementary insurance policies could be offered in this framework and the first communication in the event of a claim could be made directly and immediately via the app.

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Leader in AI and Big Data

We have been recognized as the # 1 value creator in machine learning by CRISP Research as well as a Big Data Leader in Germany by numerous experts.

Technology-independent consulting

We operate independently of manufacturers. We go out of our way to find the right technology for our clients, depending on their needs, and to support them in their implementation.

Experts in AI in the insurance industry

We have successfully completed more than 1.000 AI & Data Science projects, over 50 of them within the Finance & Insurance industry.