The insurance industry faces not only the recession but a time of significant natural catastrophe events. In such times capital expenditure tends to be focussed only on those projects that are essential to keeping the company running; pay today for benefits tomorrow is a hard business case.

The flip side though is what if you get left behind? The key to lower costs, better efficiency and richer data to aid analytics is integration. Far too many insurance companies today still use software in a ‘Closed System’ environment. That is, their back office systems are only able to accept inputs from people—they are unable to talk to other applications and systems that contain data relevant to key business processes. We all know that sometimes data is re-keyed several times within the organisation.

The problem is that these processes often exist for good reason and their strategic value should not be underestimated, but the status quo won’t scale, isn’t repeatable, and isn’t sustainable. You have deployed different applications that uniquely serve certain sets of internal and external stakeholders. Each system adds specific and measurable value. The systems are not the problem. The fact that they don’t talk to each other: that’s the problem. 

 

When talking about using technology to reduce costs, most people immediately start thinking about workforce reduction. People represent a significant fixed expense to our business and the ability to reduce any overhead through efficiency is important. While this is certainly a viable way to reduce costs, it is the variable costs introduced by these inefficiencies that can have an even larger impact: and most of these variable cost inefficiencies can be summed up in one word; errors. There is a point of diminishing returns with a headcount reduction strategy. But, start focusing on helping your existing staff reduce errors, and you can be on the fast track to significantly reducing the cost of operating your business. Also, given the insurance industry’s dependence on rich, accurate data to drive analytics that support key business decisions, the importance of error reduction is even higher.

There have been many studies in the distribution sector for example of the variable cost savings associated with error reduction; saving the return and re-send shipping costs for example. It would be interesting to perform a similar study within insurance to see whether the tangible savings are only going to be headcount based. But how can you place an amount on higher quality and richer data? Unless of course it is Solvency 2 compliance.