Briefing: Data without direction is just noise

Insurance has always been a data hungry industry – accumulating and digesting data is one of the core foundations of the industry we work in and is something I have spent my career focusing on, both as a journalist and in my work at Insurance DataLab.

But the amount of data available today is increasing at an exponential rate and sometimes it seems as if insurers are drowning in data.

From telematics devices and personal wearables to online purchasing habits and digital engagement, the modern insurance market has access to more information than ever before.

Just having access to data, however, does not instantly create value – sometimes it seems as if data has become a distraction for insurers, rather than a driver of action.

This observation is paralleled in the use of generative artificial intelligence (AI). More data – and more sophisticated AI tools – are not always the answer and, even if it is, then you at least need to know what problem you are trying to solve using data first.

Data, in itself, is not the problem. The issue here is the pursuit of data without clarity on what is trying to be achieved. Without defined objectives, insurers risk collecting and analysing data simply because they can. 

This has, of course, become an easier task with the continued rise of AI and machine learning. But even with these powerful tools, using data just because it is there and available still leads to not only increased costs – both in time and money – but decision paralysis, where firms are always looking for the next big thing without ever really answering the most pressing questions.

Discrimination by data?

The most effective insurers are those that use data as a conduit to reach the solution they need – not as the ultimate goal, collecting data like pirates amass treasure.

They start by identifying what they are trying to achieve – whether that’s reducing fraud, improving underwriting accuracy, or enhancing claims experiences – and then work backwards to find the data that can help. 

But even when data is used in the right way, there is still another very real risk for the insurance industry to grapple with – hyper-personalisation.

The ability to price products down to an individual level may seem like a victory for fairness and efficiency, but when taken to the extreme, it can create an uninsurable sector of society. This then undermines the very principle of insurance, that the premiums of the many pay for the losses of the few.

The sad truth of this is that those individuals that are most likely to fall foul of this unintended consequence are those that are among the most vulnerable in our society.

You already see it in motor insurance, where the so-called poverty premium is increasing the cost of cover for those that can least afford it, and as insurance becomes even more personalised, this risk will continue to grow.

At what point does underwriting precision become discrimination by data? That’s the ethical question the industry cannot afford to ignore.

Asking the right questions

And that’s why asking the right questions matters more than ever.

Rather than chasing the latest dataset or analytics tool, insurers should be asking ‘what are we actually trying to solve? Who benefits from these new propositions? And what are the potential unintended consequences?’

The firms that will do well in this new data age won’t be those that have the most data, or the fanciest algorithms. They’ll be the ones with strategic intent, ethical awareness and a clear focus on impact and outcomes to make the most of these tools.

Because without direction, all that data is just noise.