Insurers to implement machine learning to drive profitability

Insurers to implement machine learning to drive profitability

Willis Towers Watson has included machine learning in the latest edition of its Radar pricing software

Richard Humphreys |

Willis Towers Watson has released an updated version of its Radar pricing software, Radar 3.0, which features new analytical techniques in response to increasing demands from property and casualty (P&C) insurers to embed more sophisticated pricing approaches as competition continues to ramp up globally.

“With competition intensifying, changes in regulation and distribution, and the ongoing redefinition of consumer expectations, we are seeing a clear and widespread focus on pricing sophistication and effective customer management,” explains Duncan Anderson, Willis Towers Watson’s Global P&C pricing and product management leader. “As part of that, insurers in many markets are now actively incorporating machine learning models in their pricing approaches, not only in backroom analytics but also in the live deployed rates. Radar 3.0 supports all of this.”

Building on the last major release of Radar in 2016, Radar 3.0 now implements a wider range of machine learning models, enabling a greater number of analytical techniques to deliver more effective pricing approaches. Processing speed is also up to five times faster, meaning companies can refine and test pricing approaches more rapidly.

The full Willis Towers Watson Radar product suite comprises the Radar Base modelling and reporting environment; Radar Dashboard for sharing pricing management information across the organisation; Radar Optimiser for price optimisation; and Radar Live. All are fully integrated with Emblem and Classifier, other well-established pricing analytics products in the Willis Towers Watson software portfolio.

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