"When we can see the data from different areas of our business interact, it challenges us to look at how we fund risk and allocate our capital."
In the past statistics had been the main tool used to evaluate risks in the Insurance industry. However, with larger and more detailed data sets combined with advancements in Data Science greater insights can be found using these techniques.
Methods such as Text Mining, Predictive and Behavioural Analytics combined with Pattern Recognition and Network Analysis techniques allow for Fraudulent claims identification using 360-degree customer profile analysis.
Deep Analytics of customer data provide recommender systems for products that can help with targeted cross selling.
Risk groups identified through Cluster Analysis and Classification. This type of Data Analysis allows personalised risk pricing to be automatically applied to customers.
With the vast amounts of data insurance companies have on their customers Customer Churn Prediction is a tool they can utilise by making use of Segmentation Analysis.
Efficiency is another area that Data Science can help the Insurance industry. Claims can be processed faster through Classification analysis techniques that match claim types with the correct department automatically.
In the Auto Insurance industry telematics have allowed companies to collect data and, through the use of Data Science techniques, allow their customers to sign up to personised “Pay how you drive” pricing plans.
Improve Fraudulent Claims Identification
Predict Customer Churn
Accurate Risk Pricing
Speed up Claim Processing