Читать книгу Analytics for Insurance - Tony Boobier - Страница 19

Insurers are increasingly recognizing the value in their data but are often faced with the challenge of working out how to get started, how to find and gain access to the data they need, then to convert this into a useable form. Even before they start any analysis, they have major issues in setting up the organization, systems and software to be used. Part of the complexity is in respect of the skill sets needed to undertake this journey, ranging from systems management, data management, analytical capability and the ability to translate the data and eventual insight into a solution which is ‘consumable’ by the end user.

The increased change in mood towards more ‘agile’ organizations which undertake change through a series of ‘sprints’ rather than a consequential ‘waterfall approach’ may result in larger insurers having the same dynamic approach to change as that of the smaller company. On the other hand, perhaps smaller companies will want to adopt a more ‘risk-averse’ approach until such time as technologies are proven. Smaller insurers with more to lose might perhaps approach change with a ‘second mover advantage’ view of the world, adopting safe and incremental change which provides them with greater certainty

The effective implementation and adoption of data and analytics by insurers and intermediaries will almost certainly and eventually lead to transformation of the entire industry. The first question is not ‘if,’ but ‘how quickly.’ The second question is ‘how’. Where will the change start?

The immediate thought is that change will occur initially within the larger organizations which have the funds and ambition to change. But larger firms are complex by their nature, have legacy issues to contend with, and may not have the nimbleness to change quickly albeit they may have the desire to do so.

On the other hand, smaller more agile firms with shorter chains of command may feel that the case for change is less clear, may be reluctant to incur expenditure, and simply may not know where to start their journey. These relatively smaller insurers, including specialist insurers, may also struggle to see the value of change. However, there are signs that even smaller insurers which embrace an analytical approach can grow rapidly. Specialty insurers can obtain greater insight into their existing book of business and become both more profitable and less vulnerable to volatile market conditions.

European insurance carriers will have already started their analytical journey, forced to take the first steps by regulators who have demanded that insurers improve the management of solvency. Effective management of the Solvency II program or local equivalent has resulted in insurers needing to address a large proportion of the structured data within their control, especially financial data. The timetable for Solvency II implementation may have slipped but larger ‘Tier 1’ insurers have got an early start in managing their wider data program. As a result, they may also have the time, skills and perhaps also the confidence to start looking at other data areas with greater purpose.

Another interesting conundrum emerges in that the effective management and analysis of data may start to have an equalizing effect, reducing the perceived differences between Tier 1 and other insurers. Larger insurers may become more ‘agile’ and mimic the smaller company. Smaller insurers may become cautious and keen only to adopt proven technologies which with time may have become less expensive in any event. Analytics in the virtual enterprise may also allow both larger and smaller insurers to become more confident in their outsourcing arrangements.

At its most basic, an insurance company simply comprises three elements:

1. Manufacturing of the insurance product – that is to say, underwriting, capital allocation and regulatory reporting

2. Distribution – the way that a product is brought to market, which may be directly or through a third party

3. Servicing – for example claims management and collection of premiums.

Two of these three elements need not sit within the insurer, but rather can be discharged through third parties and partnerships, leaving only the ‘manufacturing’ of the product to be undertaken. As insurers increasingly identify the value of integrating their own data with that of their supply chain, outsourced and third-party activities will have the opportunity to become more fully integrated. We will have entered the era of the virtual insurance enterprise.

Because of this new ‘virtual enterprise,’ fully supported by adequate data security and privacy, it is entirely feasible that Tier 2 and other insurers will be able to compete with and perhaps even outperform their larger competitors, not only as a result of more effective use of data and analytics but by virtue of their greater flexibility and nimbleness.

Intermediaries will also have a part to play in this story. They will also need to develop data and analytical capabilities just to stay in the game. Supply chain experts will demand these capabilities from their supply chain simply to allow them to stay in the procurement process. Through this change which is likely to be driven by the procurement process itself, the insurance industry is increasingly likely to see the emergence of the ‘super supplier.’

It follows that as procurement experts will be involved in the setting of the data and analytics requirements of their vendors, then the procurement professional will also need to have knowledge and insight into available analytical technologies. The supply management professional seems already to have many of the characteristics of an analytical professional especially in that part of the industry known as ‘category management.’ These particular experts use data and analytics in either spreadsheet or proprietary forms to understand vendor capacity, process and response times, costs and pricing and contingency management. These seem to be valuable analytical capabilities which may be of wider benefit to the insurance industry downstream as the analytical maturity of organizations increases. With an anticipated skill shortage of analysts predicted not just in insurance but across the wider business world, might supply chain professionals have a future part to play?

Taking all this into consideration, the insurance picture begins to transform. Existing business models start to be stretched into areas which a decade ago were probably inconceivable. The traditional value chain starts to break down, replaced by other perhaps loosely coupled contractual arrangements and now enabled by the new data and analytical technologies.

Future underwriting is also likely to be transformed. Both personal lines and commercial underwriters will have significantly more data and information on which to make more accurate decisions and more representative pricing. Better statistical models are also likely to emerge. Furthermore, there will be improved integration between analytics, GIS (location) and the use of more sensors. The development of the ‘semantic web’ – an expression coined by Tim Berners Lee, the father of the World Wide Web, to provide a way for it to operate in a more standardized way through common data formats and protocols – will provide the insurance industry with a common framework whereby data can be shared and reused across ‘applications.’ In doing so this is likely to increasingly break down enterprise and community boundaries. The consequence of all this will inevitably lead to the role of the insurance underwriter being transformed, as well as their working environment, skill set and almost certainly their career path.