Issues around the likes of data sovereignty have meant that predictive modelling has traditionally been managed internally. However, HPE Pointnext's Dirk Anderson argues that this is more costly and makes businesses less agile, especially when major insurable events happen
The insurance sector has never been more reliant on data.
Data that allows companies to come to conclusions; data that creates competitive advantage; data that allows for new revenue streams.
The problem is that many insurers are saddled with the legacy infrastructure and processes that helped them achieve their success to date.
Which then begs the question: Why change something if it is already working? Can you justify the risk?
All of which would be a fair argument if the alternative was a small improvement. But that is no longer the case.
Take predictive modelling for example, these models can be run up to 90% more efficiently where they are refactored to reflect the potential of Cloud-based technologies.
So in order to take advantage it is necessary to look at the key issues with existing infrastructure, and then reflect on how a Cloud solution could help.
The opportunity cost of maintenance
There was an argument for many years that the best approach to a predictive modelling was to build something internally with your own team.
This meant that issues around data sovereignty and so forth could be governed by those in the know. In reality, the maintenance required to do this don’t justify the approach. And with Amazon and Microsoft’s span of global accreditation, security has become less of a concern.
In addition to the issue of assigning resources for scheduled maintenance and so forth, these infrastructure teams have high opportunity costs.
In contrast, when looking towards the benefit of distributing computing in the Cloud this can lead to an estimated 200% increase in productivity for IT teams, better valued innovation and the freedom for modelling teams to become pro-active, rather than re-active.
The deadly cost of downtime
Predictive forecasting can save insurers millions of pounds in the event of natural disasters - but with that said, there are still many organisations that fail to respond accordingly to the reality of downtime.
Like most industries that are reliant on technology, downtime, especially related to consumer activities, can be incredibly costly both from a financial and brand perspective.
When in the Cloud, modelling platforms can batch their processes, run at all hours, and use a global network of data centres to avoid failure.
The timely cost on scaling
While there is continuous work that must be done from a predictive modelling standpoint, the greatest potential wins exist with regards to scale.
Presently when this modelling is done on site, this involves calculating peak workflows at any point during a 12 month period, and then estimating the server capacity to match that. Thereafter, should more work be required individually priced ‘compute’ is ordered, and goes through a lengthy process of approval.
With distributed computing in the Cloud, organisations can scale accordingly when it is ‘business-as-usual’, which could mean utlising certain time zones and locations that are cheaper off-peak. But more importantly, during busier periods where more calculations are needed, such as a weather event, it is easier to upscale to access the necessary ‘compute’ needed.
From a purely financial perspective, it wouldn’t be economical to have that degree of scale running at all times, which is why Cloud budgets are often tightly monitored. But equally, it is better to have that scale and not use it, as opposed to the alternative.
And this is one of the key factors that allows one insurer and not another to appear victorious in times of major incidents, because they can most effectively respond, and alter risk variances, in close to real-time.
There are so many relevant factors, and benefits associated with cloud-based predictive modelling. The next step is yours.
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