Using data analytics to optimise insurance and risk management

13 December 2015

Data analytics can help you to identify and address discrepancies between your risks, risk management and insurance spend.

‘Big data’ has long been a buzzword in the insurance industry, like many others. But what does it really mean for your risk management and insurance spend? 

Ultimately the importance of ‘big data’ lies in its quality and how it is used – commonly referred to as ‘data analytics’.

This can help you get to grips with the efficiency of your risk evaluation and insurance process. 

Often it shows that the most serious risks might take the largest share of premium but, too often, absorb the least amount of time in terms of your risk management. 

Data analytics helps you to see these discrepancies, often for the first time, and synchronise the weighting of:

a) risk with 

b) time spent managing the risks and 

c) insurance premium spend.

Even if it is not possible or appropriate to entirely synchronise these three factors, this insight will help you to better understand why they are not in sync. 

Cutting costs

In the cost-conscious world we all operate in data analytics also has wider benefits.

For example, you might be tasked with reducing costs as part of a general cost-cutting programme. Analytics helps to do that intelligently, providing a transparent rationale for insurance buying decisions that can be shared with your board and supported by clear numbers.

Analytics can show the increased volatility associated with retaining more risk against the cost of transferring it. So your board could see those numbers and make decisions against their own corporate and financial goals.

Quality, creative data analytics also brings a wealth of new opportunities to benchmark against your peer groups. As more boards demand that risk management and insurance purchasing decisions are made on a demonstrably analytical bases, being able to show how your claims and losses history, risk management and insurance spend compares favourably to other companies will be a real asset. In the long term it could increase your contribution and influence within the company.

Hidden threats

The biggest prize from using data analytics is often understanding hidden threats. This could be risk aggregation following a period of merger and acquisition, especially if you are a multi-national company, or failing to spot a supply chain vulnerability. 

Often the analysis is about the amount of time spent with suppliers: your biggest suppliers typically get the most attention but sometimes a small but highly specialised and crucial relationship can be overlooked. 

As so often when data analytics is applied intelligently, the solution isn’t always “buy more insurance” but to manage risks in a smarter, more business-relevant way.

Download Risk Specialist article

For more information please contact Hamish Roberts, Business Development Director on +44 (0)20 7528 4141

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