Making the invisible visible

Meet the UK’s ‘Invisible’ population – 5.8 million people who are virtually indiscernible to the mainstream financial system, because there is little or no information available on their financial track record.

Experian has set the goal of radically reducing the UK’s Invisible population, by harnessing the power of new and relevant data sources.

This will help organisations to make more informed decisions about a significant number of potential customers who, up until now, they have struggled to serve.

Our new report explores real life case studies of people who have been impacted by this issue and presents some of the options for using alternative data sources to build their files.

Download the paper here

Why does the Invisible population struggle?

Without access to affordable and relevant financial products, the Invisibles have less choice and are forced to pay more – a “poverty premium”.

The social arguments for enhanced financial inclusion are undisputed, and meeting the needs of a larger proportion of the population allows financial services providers to fulfil their responsibilities to regulators. But there are also clear business benefits.

Financial inclusion and the accurate assessment of affordability are issues we care passionately about at Experian. It is our mission to significantly reduce the Invisible population, and we believe introducing new and more appropriate data sources is the key.

What are ‘new data sources’ and how can they help?

‘Traditional’ credit data is provided through cards, loans, mortgages and current accounts. New data sources, meanwhile, provide additional insight, visibility and transparency about all consumers, including their identity and payment behaviours.

Non-traditional data sources are growing in importance given the rapid and ongoing evolution in the way people manage their money, transactions and credit.

For the individual, using alternative data has the potential to help expand responsible access to credit for people who lack the traditional information to strengthen their credit history. Someone without a loan repayment history on their credit report, for example, might pay other bills or recurring charges on a regular basis. These payment histories could demonstrate to lenders that the person will repay a debt as agreed.

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.

By adding further new data sources, along with Open Banking information, more than 1.5 million people could be financially included.

What are the business benefits?

By helping to make this ‘invisible’ population visible through thickening their files, businesses can open up a significant potential customer base.

Alternative data:

  1. Improves assessments of creditworthiness and ability to repay the debt
  2. Provides up-to-date, real-time information
  3. Can lead to better service and convenience: Some kinds of alternative data may allow lenders to automate tasks
  4. Lowers costs
  5. Reduces levels of risk
  6. Provides companies with a way of demonstrating to their customers that they are responsible businesses

What can organisations do now?

Our paper sets out what organisations can do to help reduce the Invisible population and serve a wider range of customers than ever before.

  • Assess your organisation’s view of its customers. What information could help you to get a more complete view of your client base?
  • What information does your business share with other organisations? If you share data with other providers it could help people get improved access to services.
  • Which groups are you under serving? Whether groups of people are not on your organisation’s radar at all, or currently marginally declined, new data sources could be the answer to make better decisions.

Who are the invisibles?

Jack Monroe

Jack Monroe is a British food writer, journalist and campaigner. A single parent, she rose to fame with her blog, ‘A Girl Called Jack’, sharing cheap recipes to feed a family for under £10 a week. Jack has experienced recurring setbacks in her credit ‘journey’ and often struggled to make ends meet.

‘If I hadn’t had a thin credit file my whole story may have been different. I continue to be a ‘thin file’ even though I’m a bestselling author. Even now I can’t get an overdraft.’

 

 

 

Sandra

Sandra is a practice nurse, who divorced after 28 years of marriage. During this time, their mortgage and all bills had been in her husband’s name. This left her with no credit history, and unable to take out a mobile phone contract at a time when she was trying to rebuild her life and her independence:

‘Getting turned down for a mobile phone was awful,’ Sandra recalls. ‘It was so embarrassing to be told that, despite having always paid your bills on time, you have no credit history. Then I needed to apply for a loan. I was petrified I’d get turned down for that too. As it was, I could only apply for a high interest loan.’

 

 

Kharis

Kharis moved from Bermuda to study in Bristol seven years ago. Awarded a full scholarship, she always made sure to pay for everything ‘up front’. As a result, despite being debt free, she still has no credit history in the UK. This has made even the basics, such as renting a room, very difficult.

‘I have found plenty of places I can afford, but – despite having rented for 3 years – am never eligible. I know that there are many other people like me, in ‘limbo’ – with the means to be financially responsible but without the necessary data on file.’

 

 

LiPing and Mark

Mark and LiPing, professionals with a young family, had been renting for 12 years without missing any payments. They were confident that this track record and their ability to get by without credit cards would stand in their favour when applying for a mortgage: ‘As it turns out, that meant nothing at all,’ explains LiPing. ‘We thought we had been clever by avoiding credit. But we were rejected for a mortgage because there was too little financial data on my husband.’