By Tom Murray, Head of Product Strategy LifePlus Solutions at Majesco
The end result is that the insurer ends up with significant levels of information about their customers, levels of information that other industries could only dream of. In theory, this should give insurers a major edge inn understanding their customers and retaining them and make it easier to cross sell other products that would be in the customer’s interest.
But have large amounts of data is only useful if one is intelligently analysing it. And there the difficulty for insurers lies; given the huge volumes of data it isn’t easy to sort the wheat from the chaff and derive useful insights into customer behaviours and needs. Investment is required into the kind of tools that can process this information and glean the key data nuggets and patterns from within the cacophony of noise this level of data produces.
This is not an area that can be approached casually as an afterthought. The company needs to completely refocus itself to become data driven. Each project needs to be assessed for the data it will bring and existing processes analysed in the same way. A dedicated team is required to devise a proper data management and analysis function within the company and needs the right tools to complete the job.
But it needs more than a team of individuals. The volumes of data that are possessed by life insurers means that it is practically impossible for people to identify the patterns that swirl below the surface of their data lake. For this, the only realistic option is to invest in the burgeoning field of AI tools in order to be able to spot the patterns and make intelligent product and process design decisions based on the real-time behaviour of customers.
To achieve this, insurers need to invest in digital 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. This would simplify the underwriting of complex and variable risks and allow 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.
Becoming a data-driven organisation isn’t simple. It takes time, effort and investment in order to be in a position to exploit the vast amounts of information held within the life assurance company. But those who make the effort will be rewarded in taking a pole position in the race to become a winner in the digital insurance market.
|
