CRIF provides scoring models and expertise, empowering business analysts, from beginners to advanced modellers allowing them to develop, build, test, deploy and manage predictive models.
Predictive analytics allow insurance companies to extract information from existing data in order to determine patterns and predict future outcomes and trends. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment.
Today, new methodologies, such as machine learning and Genetic Algorithms to solve problems and find indicators or Evolutionary Neural Networks and the availability of unstructured, social network, and behavioural data are used to optimise the relationship between information.
The application of knowledge discovery and data mining through the use of Link Analysis and Neural Networks enables identification, analysis and visualisation of patterns in data; embracing intelligent technology to auto learn and inform processes.
CRIF has been investing in research and development to design advanced techniques in risk profiling and develop our DNA of Analytics 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, information discrepancy, to generate a more powerful and accurate predictive risk profile.