" class="no-js "lang="en-US"> Avantia boosting bottom line with machine learning - Fintech Finance
Wednesday, May 22, 2024

Avantia boosting bottom line with machine learning

Avantia, the technology-enabled insurer behind HomeProtect, today announces a major update to its retail pricing model: employing Bayesian optimisation as an alternative to traditional A/B testing.

The pioneering new approach uses machine learning to more quickly identify the optimal price for its products. It achieves this with fewer rounds of iteration and with less reliance on human involvement and the biases that entails.

This is an important commercial move from the company, which anticipates a 10 per cent increase in yield, coming from both incremental sales and an increase in revenue per sale.

Mark Eastham, CEO at Avantia, says: “This is a big step forward for us. Traditional methods of retail pricing can be costly as they take too long to hone in on an optimal value. This new technique changes the game by allowing us to test more prices more quickly. And also to identify otherwise unexplored price points with potentially higher yields. This all means that we can locate our optimal price points very quickly indeed.

“And it’s great news for consumers too. The approach is highly customisable and allows us to always offer the most competitive prices to all of our customers.”

The move represents the next step in Avantia’s strategy of using technology to solve challenges in difficult non-standard areas of the market – and deploying solutions at scale throughout its offer. Through this approach, Avantia has grown quickly, experiencing 17 per cent compound annual growth in revenues over two years from 2017-2019.

Eastham concludes: “At Avantia we are impatient. We’re at our best when we blend an intense focus on data science with our high volume, dynamic trading culture. Too often insurers are like scientists in a lab waiting for the experiment to end. We can’t afford to work like that

“And we think there’s a better way. Superior machine learning technologies, coupled with vast proprietary data and a focus on using open-source skills, allow us to trade the market. We identify hidden inefficiencies and move swiftly to act on insights before others can locate them.”

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