Articles - Machine learning is key to individualised insurance

Actuaries have always assessed the past in order to predict the future. However, forecasting the future is very labour intensive; they need to crunch through the numbers and establish the likelihood of death occurring and thereby work out the risk parameters of different products in order to maximise profitability whilst delivering value to the customer. Much of the work involves identifying patterns in the data to explore that can enable them to predict the likelihood of re-occurrence.

 By Tom Murray, Head of Product Strategy for LifePlus Solutions at Majesco.

 It was an extremely time-consuming task and much prone to mathematical error, prior to the arrival of computers with their ability to crunch the numbers at speeds way beyond the ability of a human. Computers took a lot of the donkey work away from actuaries leaving them free to examine the data from multiple angles to derive new insights.

 Machine learning, where the numbers are crunched by intelligent algorithms which can identify new patterns and thus give new information regarding future probabilities, is a huge boon to the actuarial profession. Life and Pension companies have enormous amounts of data that is often dispersed and difficult to draw inferences from. Linking it all together gives a huge data pool from which accurate analysis of the behaviour of people and the performance and suitability of products can be assessed. Trawling through this huge data pool is still major effort, even with computers, and this is where machine-learning algorithms can be of major benefit to actuaries. Machine learning algorithms can detect patterns that were never noticed before and bring them to the attention of actuaries, allowing deeper analysis of the data. In turn this can inform the design of new and more suitable products and can hone the pricing to ensure that the most competitive products can be put on the market.

 Platforms that incorporate machine-learning algorithms to constantly analyse the data would allow the insurers to provide up to date pricing changes as the underlying information grows, allowing the production of more real-time insurance capability. This would reduce the overhead of underwriting complex and variable risks, allowing companies to scale their underwriting easily and with more confidence. But platforms with machine-learning embedded can do much more. Ongoing real-time analysis of the interactions of customers and potential customers with the company can identify their behavioural patterns. These could therefore drive efficiencies and improvements in the interactions, ensuring that bottlenecks are identified and eased, encouraging more potential customers to move straight through the process to purchase.

 The benefits of machine learning that have already been applied to standard consumer retail can also be made to work for the more complex area of financial services sales and servicing. However, platforms for the insurance industry are still at the early stage, due both to the complexity of the need to be satisfied and the volumes of data involved in the whole financial services sector. This has to be the focus of software suppliers going forward, as the volume of data that can be mined gives huge potential.

 Machine learning algorithms could help actuaries by spotting patterns right across the lifecycle of the product from the recommendations and sales side through to the claims process. The information gathered can then be analysed by the actuaries to help define the life and pension product sets of the future, far more closely honed to the needs of the consumer and allowing real-time risk adjustments that better reflect the changing day to day requirements of the individual consumer. As the world got used to consumer retail on demand and entertainment on demand, the demand for the individualization of other business sectors, including financial services is growing. Already much of the consumer banking market is adopting a far more individualized approach in the services delivered, powered by AI and machine learning.

 The life and pensions industry needs to follow suit. Platform providers to the L&P industry need to be embedding machine-learning into their services from the design phase, so that the full benefits of these powerful algorithms can help insurers meet the more demanding requirements of the next generation, by providing the capability of more individualised insurance. The future belongs to those companies that can hone their pricing in real-time to reflect individual risk better and thereby provide the most efficient pricing for their customers.

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