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.
Opportunities for using artificial intelligence in insurance
New Business Models
Unlock new demographics using digital products and channels of distribution.
Reduce Costs
Significantly reduce expenditure of resources through process automation.
Improve Customer Experience
Identify and react to customer needs with the help of Data-driven solutions.
Examples of how artificial intelligence can be used in insurance
Simplified Underwriting with Predictive Analytics
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.
Customer Clustering for optimal Approach
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.
Second Medical Opinion
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 Home Concepts for Property 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.
References
Don’t miss the leap into digital transformation
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 to generate real added value from your Data.
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Use Case Examples from our Customers
The Data & AI experts at Alexander Thamm have already successfully implemented over 100 projects in the finance and insurance industry.
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
- 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 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
- 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
- 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
- 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
Data & AI projects for the insurance industry with [at]
We have used our experience from over 1.000 projects in the last 8 years to develop a holistic system for Data & AI projects – our Data Journey. An integrated Data Strategy forms the foundation and the framework for generating real added value from Data – what we have dubbed Data2Value. Our Data Lab is all about speed! Their main goal is to test Use Cases as quickly as possible – from the concept phase to the prototype using real Data. In the Data Factory, Use Cases are industrialized into finished products. The absolute main focus is on scaling and the sustainable generation of added value – as such, the user is just as much the focus here as well. In our DataOps we continuously operate and maintain your platforms and machine learning algorithms.
3 REASONS FOR CHOOSING YOUR DATA & AI EXPERTS
FROM [at]
Leader in AI and Big Data
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.
Data & AI Workshops for your team
Roadmap
You have a data strategy and want to learn how to derive use cases from it?
In Roadmap Workshops we start with a Stimuli Session in which we present relevant Data Science Use Cases. Using design thinking and brainstorming methods, we identify suitable use cases and then prioritize them. The existing business questions are then translated into data-driven questions.
Hackathon
You want to test a selected use case and pre-develop a prototype?
Starting from the status quo, the hypotheses to be analyzed are validated. Then, a first prototype is pre-developed and the initial results from the analyses are processed. Finally, it is ensured that the feasibility of a later project is given and recommendations for action and next steps are agreed upon.
Use Case
Do you have a Use Case and want to find out how to move ahead with AI development?
After presenting the status quo of the current use case, we use a design thinking session to generate hypotheses in a data science context. For this purpose, we then validate and check the necessary data to ensure the feasibility of the current use case. Finally, we develop an analytical concept for the use case.