Using enhanced income verification to inform affordability and qualify risk

In the past, lending decisions have generally focussed around the issue of credit risk, which really means: ‘If you are doing okay with the credit that you have today, then let’s give you a bit more and see how you cope with that’. Income is not predictive of credit risk in its own right and because there is no reliable way to ascertain disposable income you’ve tended to focus on willingness to pay rather than ability to pay, to the extent where some lenders didn’t even capture income as part of their decision process.  Undoubtedly, you’ve moved on. It’s safe to assume everyone across the industry agrees you need to focus a lot more on affordability. Not least because of the easy availability of finance, plus the fact that people tend to be overly optimistic about their financial future, means they can get themselves in trouble.


So how do you tackle affordability? First, you ask the applicant how much they actually earn – and that’s where you come across the first issue. Typically, individuals applying for credit may be tempted to inflate their income, so you need to verify what their income actually is. This is where Experian can help.  Income is estimated from different sources and taken from previous credit applications, then compared to the income provided by the applicant to ensure consistency. Once you have qualified their income, you can then calculate disposable income and assess affordability. Using both income and disposable income we are able to provide debt-to-income ratios which look at total borrowing as a proportion of net or gross monthly income or disposable income. These ratios can then be included in the decisioning process as part of both risk and affordability assessments.

Verifying income

Experian offers three levels of affordability solution, depending on the data you share with us. The best, gold-level solutions are derived from current account turnover (CATO) data, as well as previous application and our bureau models. Obviously, this is something only banks can share. In theory, looking at credit turnover over a 12-month period will allow you to make a pretty good estimate of somebody’s income. In practice it’s not quite that simple – an individual might have multiple or joint accounts, or they may be self-employed, with an income that fluctuates from month to month. Despite this, CATO data is still extremely useful.


One of the benefits of CATO is that there is very little friction involved in accessing the data which makes it easy to incorporate into a customer application experience because data can be processed on the principle of Legitimate Interest. Unlike Open Banking,  CATO does not require the consumer’s consent to source and use the data in a credit application.


The next best source of income is to look at previous credit applications and see what income has been declared as part of those. This isn’t an independent verification, but it is helpful to know the level of consistency shown by a customer when declaring their income. Once you understand the income, you can use it to verify income as outlined above – by comparing it to the income provided on the application form.  Generally, if the estimate is within 10% of the amount stated on the form, you can consider it verified.  Many lenders look at income verification at two levels, partly because of the complexities of joint accounts. As well as, looking to verify the income of the main applicant, you also look to verify the household’s total income.


If you’re not a bank and don’t qualify for the gold-level product, you can still benefit from CATO data through something calling the CATO warning flag. This compares the income provided on the application form against the CATO estimate and returns a ‘flag’ to indicate that the income has been verified, is close to, or is significantly different from, the CATO estimate. It means anyone can benefit from CATO data and get some reassurance that the applicant’s stated income is reasonably accurate. To help you satisfy your level of risk we allow you to define what constitutes income verification, via flexible tolerance ranges. Finally, if you’re not in a position to share income, there’s the bronze‑level solution. This models income from an individual’s credit bureau data, using factors such as credit card limits and the amount of mortgage held, to predict income.

Calculating disposable income

Once you have an income estimate that you’re happy it can be used to inform your lending decision. Experian’s Consumer Indebtedness Index (CII) is a good place to start. It uses bureau data to measures an individual’s level of indebtedness, in particular identifying those individuals who are up to date with their credit repayments at present, but who, as a result of their levels of indebtedness, are likely to experience payment problems in the future. Our enhanced Consumer Indebtedness Index, which adds in variables such as debt-to-income ratios to strengthen index results. Both the CII and the enhanced CII will identify some groups with extremely bad rates, comparable to a credit default. Many credit providers actually use high values of the Consumer Indebtedness Index as a policy rule.


As well as understanding levels of indebtedness, you’re also looking to calculate disposable income. Here, you need to be cautious, because applicants often provide a gross annual income. While you all understand how the tax rules are applied, there’s no way to account for other deductions, like Sharesave, private health cover, company cars and pension contributions. Generally, our net monthly income is not quite as high as you think it’s going to be.  To bring greater accuracy we make allowances in our calculations for salary sacrifice considerations.


To simplify your view of what a consumer can and can’t afford when it comes to monthly payments, our CATO solution is designed to provide a mechanism that marries your risk appetite with the power of our data assets to provide an insightful sliding scale of affordability.  You simply define what % of a consumer’s disposable income could be utilised by the consumer without causing concern, what % would likely cause them financial stress and what % falls somewhere between the two.  We then overlay that onto potentially monthly payments to highlight what is and isn’t likely to be affordable to the consumer. i.e £100, £200 or £300 per month is likely affordable, whereas £700, £800 or £900 is like not affordable, with £400, £500 or £600 being potentially affordable but likely needs more investigation.  The solution is designed to extend the use of current account data for non-banks into an intuitive client led risk approach.


Once you have the net monthly income you can make an allowance based on mortgage payments, which you can take from bureau data. If the individual doesn’t have a mortgage, you can use Experian’s Rental Exchange to deduct rent payments. Alternatively, rent payments can be modelled using data from the Office of National Statistics (ONS). You subtract mortgage or rent expenditure from net monthly income, then use credit bureau data to deduct other monthly credit commitments. At the moment there is limited information at a granular level on monthly expenditure, but what you do have is aggregated data from the Office of National Statistics. You use FCA best practice to calculate essential expenditure in order to derive an overall estimate of disposable income.


And that’s it – once you have a good idea of disposable income you have a much better idea of whether the individual is going to be able to meet the monthly payments on the credit they’re applying for. However, there are limitations. You have to make a few assumptions around net monthly income and monthly expenditure. While the ONS takes a range of factors into account, such as geography, whether the individual is single or a couple and whether they have children, the data is still aggregate data. It’s not data about the individual. And that’s where, potentially, open banking data will fill in the extra detail, and provide accurate, granular information about an individual’s income and outgoings.