AI Case Study
QBE group plans to measure risk at individual level to calculate optimum premium price using machine learning
Australian insurer QBE plans to deploy Cytora Risk Engine to analyse insurer’s internal data such as claims along with external data like public reports and APIS to calculate a risk score. Using this they plan to personalise premiums for individual customers.
Industry
Financial Services
Insurance
Project Overview
"QBE Insurance Group (QBE) announces today that QBE Ventures has closed an investment into Cytora, a three year old London-based start-up that uses artificial intelligence (AI) and open source data to help commercial insurers lower loss ratios, grow premiums and improve expense ratios.
The Cytora Risk Engine, driven by machine learning algorithms, combines an insurer’s internal data on a specific cover with external information from a broad spectrum of sources. This generates a risk score, which provides enhanced insight into expected claims activity on the whole portfolio and also at an individual risk level.
The Cytora Risk Engine uses arti cial intelligence to learn the patterns of di fferent risks and loss outcomes over time and computes a rank, score, and price for every property and company in the population."
Reported Results
Pilots; Results not yet available.
Technology
Function
Strategy
Data Science
Background
"QBE Insurance Group Limited is Australia's largest global insurer. It provides insurance services mainly to Australia, America, Europe and Asia Pacific region."
Benefits
Data
"Web data, Open APIs, Public reports
Claims, Exposure, Submissions"