Navigating the Data Divide in Credit Risk Management

Despite the risks and compliance issues, a worrying number of lenders grapple with incomplete and inaccurate data. The consequences influence everything from individual decision-making to broader consumer impacts, culminating in defaults and fluctuating profits. 

This disparity gives rise to a data divide:

●      On one side, we have lenders harnessing high-quality data,

●      and on the other, those lagging behind.

But it's not just about data integrity; fair pricing also comes into play, affecting everything from consumer rates to the breadth of product offerings and, ultimately, a lender's bottom line.

Let’s get into it.👇

#1 Trusting the big players

No financial service provider would willingly opt for poor-quality data. It would be unethical and open a world of compliance and financial risk. So, why are some lenders still using incomplete and inaccurate data?

In our experience, it often comes down to believing in the big players and a bit of complacency. It's understandable to assume the top credit bureaux have everything covered. But the FCA found this isn't the case. From analysing consumer data from Experian, Equifax, and TransUnion, in 14% of the cases, only one of the Bureaux has the relevant default data. And in only 66% of cases, only 2 out of the 3 Bureaux agree on the data. (That’s a big discrepancy.)

As a result, there's a huge divide occurring in the credit market:

❌️Credit providers lagging behind: They might be using data with missing information or prioritising traditional thinking over data quality. 

✅️ Credit providers excelling: These are investing in superior analytics and data, often using multi-bureau approaches to close gaps in data. And they are doing this without paying excessively for the privilege. (More on this later.) 

There are also several new digitally-led credit providers that have significantly improved credit scoring and reduced defaults by tapping into richer sources of consumer financial data. Moving away from relying solely on traditional credit reports, these firms leverage a combination of detailed transaction histories, cash flow data, and other alternative variables to assess credit risk. And they can afford to offer better products and rates.

#2 Price vs quality wars

For some financial providers, the cost of credit bureau data will be one of the biggest overheads. This often leads to lenders paying more (than they should) for comprehensive data, while others opt for lower-cost data that can be rife with data gaps and inaccuracies. (Not ideal.) 

Paying excessively for high-quality data is a common misconception: paying more doesn't mean better data. We've worked with hundreds of credit providers who assumed paying premium prices for access to bureau data is the only option available to them – that's not always the case. 

But, unless you've got access to data benchmarking to assess the entire market, you wouldn't know this. Such a lack of transparency remains in credit risk.

A word of caution here: Out-of-date data and missing key details means greater financial risk in the long run. It can have major impacts on profit, consumers, and reputation.

#3 Set and forget

Once you’ve gone through the lengthy process of bureau credit data integrations and scorecards, many lenders “set it and forget it”. But this is where data issues can occur. For instance, you might continue to use one of the top bureaux, assuming they don’t have data gaps.

Yet, as we’ve seen in the FCA findings, these data gaps are apparent. For example, this might be standard data changes, such as address, income, and marital status changes. So some bureaux will have indate/out-of-date data depending on data sources and processing efficiencies. 

Likewise, more complex information such as BNPL payment data is inconsistent amongst different credit bureaux too. Not only do different CRAs hold different BNPL data, but it’s not shared amongst bureaux either, which could hide the true affordability picture.

But if this is you, don’t panic. Continually monitoring the market for new data sources and then assessing how this matches your data supply is the solution. While this might be a challenge with the transparency issues across the market – this is where data benchmarking becomes crucial.

#4 Alternative and multi-bureau data

The leading credit bureaux often dominate the market, mainly due to perceived reliability and trust in their data. However, it's essential to recognise that they aren't the only viable options. Lenders have access to a diverse range of alternative data sources that can offer valuable insights, particularly when traditional data falls short.

These alternative data sources include, (but are not limited to):

●      Vulnerability data: Understanding who is vulnerable can provide crucial insights into a customer's financial stability and aid the treatment and adopt correct considerations.

●      Property data: Changes in property values will be something to keep a close eye on over the coming months.

●      Movers data: Tracking residential moves can offer insights into changing financial and living situations.

●      Contact data: Reliable and up-to-date contact information is vital for contact strategies.

●      Land Registry information: Insights into property ownership, etc. can be a significant asset.

●      Financial stress indicators: Factors such as loss of employment or salary changes can signal a shift in a customer's ability to repay.

●      'Buy Now, Pay Later' (BNPL) data blocks: This emerging data set offers a view into modern spending and credit habits.

●      Loan application data: The information gathered during loan applications can provide a comprehensive view of a borrower's financial health. 

By integrating these alternative and multi-bureau data sources, lenders can gain a more holistic view of their customers. Historically, credit providers have been reluctant to use this approach because of the perceived cost of doing so. However, this is somewhat of a myth now. With data benchmarking credit providers can get more flexible contracts and discounts. Bulk-buying is no longer essential.

The bonus point: all this data plugs gaps and verifies data from a primary bureau. Of course, the additional data sources need vetting for reliability, but there are quick and simple ways to do this.

So what are leading lenders doing? 🔍

Juggling quality and pricing is somewhat of a balancing act. But it needn't be a case of choosing one or the other. In fact, leading credit providers are leveraging high-end data capabilities, accessing premium sources, and using rigorous controls for better risk management and decision-making. 

Plus, this puts them in a position to gain a competitive advantage through more favourable rates and greater product offerings for consumers. And even decisions can be made faster – which is what consumers look for everywhere now.

Here are some examples of cost savings that credit providers have made without compromising data quality. It all comes down to knowing what others are paying for the same data footprint and negotiating on price:

🟪 Banking: £2,000,000 saving over 5 years (28%)

🟪 Banking: £5,100,000 saving over 3 years (40%)

🟪 Banking: £2,800,000 saving over 3 years (26%)

🟪 Finance Retail: £3,000,000 saving over 5 years (23%)

🟪 Finance Motor: £450,000 saving over 3 years (32%)

🟪 Finance BNPL: £400,000 saving over 2 years (30%)

🟪 Utility: £50,000 saving over 1 year (33%)

🟪 Oil: £6,000,000 saving over 3 years (50%)

That’s how to make your bureau work harder.

Closing the data gap

Today's credit risk assessments call for lenders to keep a close eye on data quality. The FCA has even highlighted that the leading bureaux don't always mean complete data.

As we've highlighted, higher prices don't always equal better data – plus, there are ways to see how much others are paying for the same data footprint. We’d go further and add in that multi-bureau and alternative data sources needn't be ruled out. They can hugely benefit decision-making.

Without new thinking – like monitoring and extensive auditing – the data divide between leading lenders and laggards will just keep getting bigger. Particularly as advancements are made. And this will have a detrimental impact on risk assessments, consumers and profitability. 

Ultimately, getting a fair price for credit risk data is the driving force for many lenders. Paying fairly for quality data starts with data benchmarking—and finishes with significant savings.