Sagacity: Unlocking value with big data

Sagacity CTO Chris Stephenson says: "The relationship between AI and data is not as one-way as it sounds. The quality of AI outputs is ultimately dependent on the state of the data that underpins it"
Sagacity CTO Chris Stephenson says: "The relationship between AI and data is not as one-way as it sounds. The quality of AI outputs is ultimately dependent on the state of the data that underpins it"
InsurTech Digital speaks to Sagacity CTO Chris Stephenson, on how big data is enabling data science and AI implementation to thrive

We speak to Chris Stephenson, Chief Technology Officer (CTO) at data consultancy Sagacity, on the platform big data is providing to grow data science and AI technologies for insurers. 

How are insurers leveraging big data? 

From using step trackers to demonstrate policyholders are physically active, to proving they are safe drivers through installing telematics devices on their cars, insurers are getting increasingly creative about the data they use to assess risk more accurately and support intelligent decision-making. 

Postcode and age are no longer the primary factors – insurers are now combining more data points to get to know their policyholders much better than they have done to date. And many customers are happy to share real-world data with insurers if it helps reduce their premiums or get more personalised offers.

Legacy insurers have pools of data, why are many yet to fully leverage it?

Long-established insurers’ IT infrastructures are incredibly complex and would have typically been built over several decades. 

This can lead to sprawling legacy systems, with data scattered across multiple locations and formats, and IT disconnected from business functions. Many systems are also built in-house, meaning there is no manual you can go to for help if you want to find a specific data point. 

Combined, these factors make it challenging for insurers to obtain a single view of customers or treat them as individuals with personalised policies and product offers.

To get the most out of big data, legacy insurers need to prioritise making all customer data accurate and easily retrievable, so that it is then possible to apply analytics to uncover the insights that drive better decision-making.

How does AI and machine learning make use of big data?

Big data is defined as the increased Volume, Variety, and Velocity of data businesses hold, for example on customers, products, and profitability. 

Put simply, AI can help businesses overcome the challenges brought by having lots of data in different formats by stitching together data points to reveal trends and drive more informed, accurate decision-making. 

But the relationship between AI and data is not as one-way as it sounds. The quality of AI outputs is ultimately dependent on the state of the data that underpins it. AI is also not the ‘plug and play’ technology that many businesses mistakenly think it is. 

In reality, there are many types of AI, and choosing the right one depends on the problem businesses are trying to solve.

Take machine learning as an example – a type of AI that takes a statistical approach and relies on ingesting data to understand and make decisions. But if that input data isn’t reliable, they won’t be able to trust the output.

For machine learning to work, it must be fed with accurate, complete, labelled training data, most of which should be sourced internally – but could also be purchased from a data marketplace or acquired via publicly available sources.

How will big data be leveraged further in the future? 

The three Vs of big data – increasing Volume, Variety and Velocity – are often associated with the technological challenges in capturing, storing, and using data within organisations. 

But the most innovative companies will be those that look beyond these challenges to recognise the value big data can provide. Greater volume and variety of data can help make existing models more accurate and personalised. 

Meanwhile, increased velocity of data can ensure models are updated and risks are managed nearer to real-time.

Let’s take the example of the insurance industry. By stitching together a wide range of datasets using sophisticated analytics and AI, insurers are building models that show the true value of their customers. 

These models weigh up multiple factors including the value of a policy, years with no claims, and loyalty, enabling companies to better understand who their most valuable customers are. 

In turn, insurers can then reward these customers with discounts to retain them. Sophisticated data models are essentially putting an end to the days of drivers who have never made a claim yet pay more just because of their postcode.

Data models are also enabling insurers to better manage risk and identify vulnerable customers. 

Combining internal records on billing with third-party data on benefits, pensions, and household composition helps insurers monitor customers’ situations in real time. 

This means companies can offer special tariffs or support to ensure vulnerable people aren’t priced out – which is increasingly important against regulatory pressures such as Consumer Duty. 

The most innovative businesses will even use AI to create models that predict future risk, to intervene before customers reach breaking point.


For more insights from InsurTech Digital, you can see our latest edition of the InsurTech Digital here, or you can follow us on LinkedIn and Twitter

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