Describe Data's Kompreno Risk Engine consumes data from many different sources and can be applied to all areas of insurance.
Our team of PhD data scientists, actuaries and IT professionals has over 50 years' insurance industry experience.
We augment your data with intelligently sourced public datasets to gain you an edge on the competition.
We use curated data from industry databases and other sources and overlay it over your data.
We use machine learning tools and techniques with natural language processing and AI to understand unstructured data.
We are acknowledged experts in the use of Bayesian statistics. Insurance is a Bayesian business.
We help to create expert in-house capability and embed a culture of scientific data discovery and communication.
By combining gold standard vulnerability data with constantly updated relevant social media feeds we have developed a cyber risk product that allows insurers to price and understand their exposure to cyber risk.
We use best of breed external data sets, social media feeds with tools and techniques derived from epidemiology to build quantitative models of global terrorim risk that are easy to interpret and apply.
Using open-source and freely available UK data we built a detailed geo-spatial model of the UK population. Initally developed as a tool for large employers to understand their workforce our tool has applications in healthcare, marketing and many other industries.
Combining securities class action legal documentation and detailed financial company information we used AI and Natural Language Processing techniques to build a deep understanding of the universe of Directors & Officers insurance.
With over 50 years of insurance experience the Describe Data Executive Team have a wealth of experience.
Formerly the Head of Data & Analytics at Barnett Waddingham Michael has worked in actuarial and IT roles. He brings a business-first approach to the use of machine learning and is as well advises businesses on how to efficiently establish data science functions.
Mick is a physicist with a MSc in high performance computing and a PhD in quantitative finance. He uses techniques such as time series analysis and Bayesian methods to help insurance companies with problems that traditional actuarial approaches struggle with.
Gerard has many years’ experience building and managing technology platforms for international companies in reinsurance and insurance. He combines a passion for technology with international business experience and a pragmatic approach to getting results.