Each speaker explains how they are using machine learning in their firms and what they feel the benefits and limitations are, for example at Zenith, a proof of concept was conducted with a firm called DataRobot around market pricing to create a market model. Then a prediction was made on the average of the top five quotes using factors such as age, occupation, postcode, marital status, licence held length, NCD. Information was obtained from comparison website Money Supermarket and then the market model was trained using machine learning techniques.
For Markerstudy their first area of focus has been the cybersecurity space, with a view to protect its operational stability. The threat detection ware being used learns to identify the formal traffic movement through your organisation and pinpoints any anomalies which may be a threat. As Head of Claims Counter Fraud at Direct Line, Bob reviews masses of data around claims and third party data, his firm uses machine learning to overcome the challenges with traditional defence tools requiring periodic retunes to review the data. Machine learning constantly tunes itself and tracks movements and behaviour changes by looking at the outcomes.
Victor Hu from QBE adds a new perspective on machine learning with his original experience of the technology bring from his time in the music industry, he outlines the importance of using a human touch "The potential benefits of machine learning are pretty vast. Detailed decision-making based on a number of factors and different data points is really at the heart of what insurance is about. So machine learning is an additional tool to enable professionals to make more accurate decisions. "
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