Analytics for Insurance. Tony Boobier

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СКАЧАТЬ 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.

      1.3 How Do Analytics Actually Work?

      It is in the nature of insurance people to want to know how things are done. They want to understand how business intelligence happens from a technological point of view, how predictive and prescriptive intelligence works and what really sits beneath the covers in cognitive analytics. That is not to say that they want to be able to do it themselves, but rather in understanding the basic mechanics they are able to recognize the key issues and also the limitations of the technology. It will also help them in terms of the implementation conversation.

      Let us start by saying that this is not a simple matter nor was the concept of Big Data and Analytics invented overnight. Rather that the insurance industry finds itself in today's analytical environment as a result of evolution, sometimes also an element of the step change and also from time to time, as a result of different thinking. In insurance which already has a legacy of analytical thinking as a necessary result of actuarial processes, new ideas increasingly find their way into the industry from other sectors such as retail or telecom. The use of ‘Smart Meters’ and predictive maintenance of machinery is already present – but how can this thinking be adapted and extended to the insurance sector? What comprises innovation for the insurance sector may be relatively ‘old hat’ for other industries. For insurance practitioners, it is critical that they maintain a 360-degree view of all that is happening in the wider world of analytics to be able to take full advantage of the opportunities before them, and then to be able to take that thinking and apply it to their own industry.

      1.3.1 Business Intelligence

      The starting point of any discussion regarding business intelligence arguably goes back to the concept of measurement and control. Without measurement there can be no control, and without control there can be no improvement. Such straightforward thinking found its way into the challenges of industrial productivity of the automotive and other manufacturing lines of the 1920s and later, and was subject to continuous refinement both in process and methodology. As organizations drove for increased profitability, the management of activity and its translation into activity-based costing (which identifies activities within a process and assigns cost to each activity) started to dominate, and up to the present day still heavily influence our thinking. Cynics reasonably argue that cost rather than value is being measured, and that the measurement process drives a quantitative rather than qualitative agenda.

      But regardless, the essence is that measurement of operational activity in the form of performance metrics became prevalent and to a great degree remains so. What organizations have come to realize is that the metrics which drive performance improvement also drive changes in behavior, and that these changes are not always beneficial. Individuals measured against performance metrics often seek ways of manipulating data to show themselves in the most positive possible light, for example, in the case of sales progression. The psychological linkage between performance management and individual behavior cannot and should not be underestimated. To counter this, some organizations are also building behavioral traits into the assessment process, although like ‘soft benefits’ these behavioral traits may struggle to avoid a degree of subjectivity.

      The topic of ‘conduct risk,’ that is, how we manage the performance and behavior of our sales people for example, becomes increasingly important especially in the shadow of Dodd-Frank and other consumer-orientated legislation. Analytical capability can sit behind the sales process not only in terms of sales performance management but also in the way that sales are conducted. If performance metrics drive behaviors (as might remuneration packages of sales staff) then analytics can be used as part of the solution in ensuring sensible behavior.

      In essence, information collected can be assembled and structured to create management information. Historically this has been through tabulation but increasingly has been managed through spreadsheets. Information from outside the immediate organization, for example from the supply chain, can be obtained by ‘enforcing’ the supplier to provide information in a prescribed format as part of the supplier contract, so that information from many suppliers can be merged and consolidated to give a view of the broader environment. In other words, the procurement process can form one of the tools whereby suppliers provide information in a consistent way allowing the insurer to gain greater insight into multiple factors, such as cost, value and customer satisfaction drivers.

      By collating this information from many suppliers as part of an RFI (‘Request for Information’) or ongoing process, insurers and others may find themselves with a clearer view of particular parts of the industry than some of the so-called expert vendors themselves. The challenge for such insurers is to recognize that knowledge in such circumstances is power, especially in a vendor negotiation process. Procurement professionals have recognized this for some time and use it ruthlessly in the negotiation process.

      For many insurers, a spreadsheet approach to management information remains a critical capability but even spreadsheets have evolved. Those who were once experts by being adept at creating a pivot table now find themselves needing to be conversant with the advanced capabilities of spreadsheets with better visualizations and analytical capability. Spreadsheets, like the whole topic of analytics, continue to evolve and allow the user to have greater insight and improved visualization. The challenge for users of spreadsheets is most probably not only that of data capacity but also the increasing complexity of the business operation and its interdependencies. If it is argued that the insurance business is too complex to be managed either by intuition or experience (or both), then we are increasingly reaching the tipping point (if we have not reached it already) that it is also too complex to be managed by spreadsheet especially in the larger organizations.

      Business intelligence is more than a form of enhanced management information. Rather it is a fundamental tool which allows insurers to understand if they are on track to meet their strategic objectives and where appropriate provide early warning signals that corrective action needs to be taken. If the sole purpose of executives is to ensure that the strategy of the organization is achieved, then it is critical СКАЧАТЬ