Tag Archives: Neil Cantle

Can traditional insurers keep up with the InsurTech transformation?

Technology is changing the way businesses evaluate risks, transforming customer interactions, and overhauling the purchase process. As traditional insurers strive to overcome legacy systems and practices, how are they successfully keeping pace with new InsurTech entrants? In the Milliman Impact article “Setting the pace: InsurTech transformation,” Neil Cantle, Russell Osman, and Pat Renzi offer their perspectives on the challenges that traditional insurers must navigate.

 

Lapses in concentration

A range of factors interact to influence lapse behaviour as it relates to long-term insurance. Yet this is not typically taken into account directly when setting assumptions. This report by Neil Cantle and Jennifer Smith sets out the methodology and results of Milliman’s research investigation into the use of advanced systems mining techniques to determine how lapse experience for long-term insurance business might change according to the prevailing dynamics within the business and because of uncontrollable external factors.

Emerging risk analytics: Application of advanced analytics to the understanding of emerging risk

This report by Milliman’s Neil Cantle uses advanced machine learning algorithms, such as deep neural networks, to analyse social media conversations about Brexit. The purpose of the study was to examine whether useful information could be extracted from social media in what is effectively real time on a key topic in a political economy.

To optimise financial decision-making, human-and-machine iterative process proves most successful

Milliman has announced that an innovative new study examining multi-criteria decision-making using an iterative process of advanced computing and human input has shown superior results in risk management when compared with machine algorithms or humans alone.

Using an illustrative example from the life insurance industry, the study looked at how optimisation techniques can be used to develop insights into drivers of economic capital within an internal model framework, and how to then use these insights for risk management decisions. The findings illustrate that advanced computing, visualisation, and complex systems-mining techniques that include expert input can deliver superior optimisation results when faced with multiple objectives and multiple constraints, which machine algorithms alone find challenging to resolve.

While not obvious at the outset, combining human input with advanced computer modeling allows domain experts to analyse results and elicit insights into features that subsequent iterations of a model should contain, thereby refining the process.

Milliman’s study employed the DACORD platform from DRTS, Ltd. to support its system-mining efforts. ‘Future states are unknown, involve human affairs and are therefore complex,’ says Jeff Allan, CEO of DRTS, Ltd. ‘Augmenting experts with the appropriate tools and processes can aid the reasoning and evaluation of a range of solutions.’

Adds Milliman’s Corey Grigg, ‘Looking toward the future, this sort of optimisation technique can extend to big data, simulations, and enhanced visualisation, ensuring that even as the complexity of our data and problems increases, experts can continue to add value.’

The results suggest a number of practical applications for enterprise risk management (ERM) in the insurance industry, including finding patterns in key risks driving capital losses and understanding diversification in order to enable quick judgements about the similarities and differences in the risk profiles of different portfolio elements.

Milliman’s Optimisation study was conducted in conjunction with Dr. Lucy Allan of University of Sheffield. To read the entire study, click here.

Big data challenging how insurers think about business

The insurance industry has a long history of using data to make decisions around risk. However, as more and more data on risk becomes available, insurers will encounter numerous business challenges. In the Milliman Impact article “Harnessing the transformative power of big data,” consultants Neil Cantle, James Dodge, and Derek Newton offer perspective on big data and its implications for insurers’ business models, data governance, and skills moving forward.

Innovation and technology creates a new market for players and new challenges for insurers

Innovation is changing the insurance industry landscape. To remain viable, traditional insurance companies must transform their business models to meet new data and consumer expectations. The Milliman Impact article “Disruption or innovation: A digital future for insurers” explores some technological advances that are opening up the market to new players and challenging insurers to augment their approaches.