Data science for insurers
From claim management and customer churn prediction to risk assessment: the applications in which data science can generate knowledge advantages and thus competitive advantages for insurers are manifold. We are specialized in the field of data science and have been successfully developing solutions for artificial intelligence and machine learning for insurance companies for over 10 years.
Beyond the implementation of data science projects, we support insurers in setting up the right IT infrastructure for the professional use of analytics in a strictly regulated environment. With our data science training courses, we enable them to independently exploit the existing data potential.
A selection of questions we have answered in our projects in the insurance industry:
How can the data centricity of an insurer be increased?
Distributed data repositories and silo thinking: develop a concept for creating more consistent data pipelines, optimal location of data science within the company and a strong data strategy.
How can attempted fraud be detected early on?
Fraud prevention: development of an algorithm for the detection of irregularities in the distribution of the numerical structures of statements with warning function for the employees involved.
How can KPIs be optimally forecast?
Reliable forecasting of key company figures through a combination of different analysis methods. User-friendly integration of the forecast into an existing app for the responsible employees.
Which customer is particularly affinity for an insurance product?
Next-best-offer: determination of an affinity score for each customer with regard to their potential interest in a specific insurance. Knowledge base for sales and associated with a significant increase in response rates.
How can customer loyalty be systematically increased?
Customer churn prediction: identification of customers willing to leave by means of "time-to-event" analyses in order to approach these customers in a targeted and proactive manner with better offers.
How can existing analyses be operationalized?
Evaluation of the status quo, concept development and implementation of an IT environment that optimally meets the strict requirements of IT with regard to operation and security.