How additional data sources can help to reduce the invisibles population

Currently in the UK, 5.8 million people are invisible to the financial system due to having little or no financial data. When an individual has limited financial data history they are often referred to as having a thin or no credit file.

The impact of having a thin or no file can result in being considered as high risk, which in turn means banks and lenders will often only offer high interest rates – if they even offer credit at all. However, the FCA is encouraging businesses to meet the needs of all consumers, even those that are considered vulnerable, when trying to get access to financial products.

The most common cause of having a thin or no file is due to a lack of data to support the individual’s spending and borrowing habits which means banks and lenders struggle to make a true assessment of affordability.


“It’s important to recognise the true price of being invisible, not only because of the disadvantages it creates for people, but because there is an opportunity for the credit economy to work better for us all.” 

Jonathan Westley, UK&I and EMEA Chief Data Officer, Experian


What other data sources are available?

At Experian, we believe that additional data sources can help to reduce the thin file population by giving lenders new information to support more informed decision making.  Historically, credit scores have often been based on traditional sources such as spend and payment history, however we are now seeing the introduction of Trended Data and the contribution of rental and household utility information to help give a more rounded view on a person’s credit file.

The Rental Exchange, working in partnership with The Big Issue Invest, was introduced to ensure that both tenants and homeowners are treated equally and fairly. We believe that having a view on rental data in the same way that homeowners have a view on mortgage data, can open up more opportunities to both the tenant and credit provider by adding additional data sources to peoples’ files. Our research shows we have already reduced the Invisible population by 765,000 through adding data from social housing tenants through the Rental Exchange, along with data from utilities companies and other sources.


Experian has reduced the thin file population by 765,000 by adding data from social housing tenants, utilities companies, payday lenders and high cost credit providers.


Another valuable source of data is bank account transaction data. Powered by open banking and the willingness of individuals to share their data, looking at bank account transactions can help to more accurately assess an individual’s affordability and disposable income. CATO (Current Account Turn Over) data looks at income verification, disposable income and income estimation. This helps you to understand what a customer can and cannot afford to make a more accurate assessment of affordability to help reduce the need for customers ending up in a financially vulnerable situation.

Trended data looks at credit files over a longer period of time, giving lenders the ability to better predict potential financial stress or appropriately reward customers who have an improving credit trend that may not yet be apparent from their score. It allows you to dig a little deeper into credit scores to give a rounder view on your thin file customers so you can correctly match products and services that are appropriate.

To summarise, access to additional data sources can help you to find more thin file customers that could be an opportunity for your business to help grow your lending capabilities. With the right tools in place, such as CATO and Trended Data, you can get a better view of your customer base whilst reducing your business risk, ensuring you are lending fairly and appropriately. Having these tools and services, can empower your business to reach more consumers and can help you build a better, more human, dialogue with your existing customers.

Our Credit 3D proposition brings together with the latest data science, machine learning and advanced analytics techniques to build a comprehensive, multi-dimensional and accurate picture of your customer credit portfolio.

For further reading, explore our credit invisibles case studies that look at real people who have experienced financial exclusion.