The ability to accurately define risk and determine policy premium has been at the heart of the insurance business since the late 17th century.

Over the years, the science of rate making across all lines of business has become ever more sophisticated and never more so than now with the explosion of Big Data. Insurers have access to more data than ever before and the ability to make strategic use of this information throughout the life cycle of customer policies is pivotal to engineering the right conditions for business success.

Modern risk profiling since the 1970s adopted predictive models based on traditional statistical techniques to arrive at a risk ‘score’ for a customer at all stages of the life of a policy (Point of Sale, Point of Claim and Point of Quote).
Statisticians translate business problems into statistical and mathematical formulation, applying statistical modelling techniques to help solve the original business problem: generalised linear model, logistic regression, cluster analysis, decision trees and expert rules are the most frequent methodologies used for this purpose.

These risk scoring approaches have undoubtedly given insurers a keener insight into risk and propensity to claim in the last 20 years.

Today, with the availability of more data (unstructured data, telematics, social network, and behavioural data) these traditional methodologies are used jointly with innovative risk assessment methodologies; as used independently they are unable to draw from these new, vast and more complex data sets.

In collaboration with our insurer clients, CRIF has been investing in research and development to design advanced techniques in risk profiling and develop our DNA of Analytics. We are drawing on information about human behaviour, lifestyles and habits and computing large quantities of both structured and unstructured data available via social media platforms and beyond, dishonest behaviour, discrepancy between what is declared and what is found on CACHE/CUE databases, to generate a more powerful and accurate predictive risk profile.