Analyze Re Pushes Actionable and Accessible Data To De-Risk Reinsurance

Reinsurance decision-making for risk pricing and portfolio optimization has traditionally relied on the ‘gut intuition’, relationships and experience of the underwriting team, with executive committees’ strategic objectives loosely guiding the scope of business. These enterprises can no longer afford to continue in this model, as they now operate in a low margin environment with significant competitive pressures from the entry of capital market players. While a lot of them have set up internal risk analytics practices, there is still an opportunity for them to harness newer big data and analytics technologies over traditional practices.

In 2013, Analyze Re, a risk analytics startup, was born to provide this category of technology interventions to reinsurance decision-making. The company has developed a risk-engineering platform to help reinsurers significantly improve their risk pricing and optimization, using machine learning to provide a real-time marginal pricing framework. In our conversation with Analyze Re’s Co-Founder and CEO Adrian Bentley, he revealed the potential for a step-change in performance, “Our pilots require our clients to have a suspension of disbelief sometimes, because the magnitude of impact on profitability we can report back on process changes is huge.” Analyze Re has shown up to 35% improvement in profitability.

The key is that it is not just technology; it’s technology combined with a change in the type of analysis performed. As Adrian said, “With portfolio theory, there are so many factors to consider, that its almost impossible for a human to review all the data—thousands of contracts or deals—in order to maximize profitability. We use machine learning to do that, and work with the underwriter to provide a best fit, based on the strategic objectives outlined.” A great way in which Analyze Re’s platform further uses machine learning is by using a feedback loop that’s often missing from manual processes, where the actual decisions made by the underwriter are fed back for the system to recommend the next set of actions to keep on track. Companies looking to employ machine learning for their data sets may look to outsource to different companies or look at having some of their employees undergo an online machine learning bootcamp in efforts to create an in-house team of data scientists and AI programmers to better read the companies big data.

We see Analyze Re bringing an innovative solution to market to push more Accessible & Actionable Data into the hands of reinsurance underwriters. However, these buyers will need a change in mindset to shift away from the inefficient portfolio management of the past.

With such big financial implications on profitability at stake, the business case for Analyze Re is strong, but the challenge will be in its ability to navigate its clients through this status quo.

This article was originally posted at HFS Research.