Over the past decade, the world has seen an explosion of online and social activity, all of which is generating vast amounts of data. Louis Rossouw surveys the future for insurance.
Value
The value generated by Big Data can be tremendous. In the UK, the grocery giant Tesco has generated great value from customer loyalty card data and it is identified as a key advantage for them. In the life insurance sector, obvious examples include predictive modelling techniques in pricing, persistency and underwriting.
The models allow the use of more and more data variables to be used to improve processes, manage persistency and improve pricing.
They are also being used in the assessment of cross-selling potentials and in the process of marketing, especially to affinity groups. In these cases, all stand to gain from the availability of more data and variables to feed models for better understanding of insurance risk and processes.
The McKinsey report identifies one of the areas where the retail industry is likely to face reduced margins due to the emergence of data availability: price transparency. As price data becomes available, consumers can more easily find the product they are interested in at the cheapest price. The emergence of insurance aggregators is possibly an example of this dynamic in the insurance industry.
Other areas where the insurance industry may gain from more data and better use of existing data:
- Decision support systems, such as underwriting manuals, are likely to gain from the availability of data to ensure evidence-based decision making. Greater availability of data means better medical research on various medical impairments, but also that insurers are better able to scour their own data to extract analysis and improve their own decision support systems.
- Underwriting engines, such as Gen Re’s COMPASS, generate data, and user-companies have much to gain from an analysis of it. For example, COMPASS features a tool that facilitates an understanding of the hurdles to straight-through processing.
Location data is also identified by the McKinsey report as being valuable and quite influential. Location data is already being used directly for motor vehicle insurance as an exposure measure (miles/kilometres driven) but also as a risk measure in terms of the type of roads driven, day/night driving and also driving style (as measured by acceleration or breaking for example). Insurers are using in-car telematics devices to accomplish this.
