Ask any mortgage or credit provider to name their top ten customer origination challenges and you’ll find these answers featuring prominently:
- Conversion rates – Am I sourcing enough suitable applicants who complete the onboarding journey?
- Cost of acquisition – How much am I paying for leads and how many credit searches am I running?
- Treating Customers Fairly – Am I making the right decision for each customer?
None of these examples should come as a surprise to anyone familiar with the online onboarding world. After all, lead brokers have been driving growth and innovation in this market for years, expecting lenders to respond quickly and in real time to the leads on offer. With capital to deploy, lenders require a steady and scalable stream of applicants to assess as quickly, cost effectively and diligently as possible. With the right technology in place, both parties can deliver a seamless customer experience. As interest rates rise alongside the spiraling cost of living, the logical expectation is that demand for credit will increase, making speed and scalability as important as ever. But as these factors fuel demand, so too will they add to the credit risk and therefore require robust underwriting decisions.
In order to quickly execute vigorous and compliant lending and affordability decisions, Open Banking has – without doubt – begun to play a more important role each year, and this trend is set to continue. However, Credit Reference Agency (CRA) data is, and will likely remain for the foreseeable future, the most effective and reliable means by which to assess an application for credit. CRAs therefore perform a critical function for lenders.
Credit reference data though, is not perfect and none of the major CRAs would claim otherwise. There is the issue of coverage: no single CRA has a credit file on 100% of the UK adult population. Moreover, no single CRA has 100% knowledge of every one of the consumers for whom it holds a credit file. Why is this? The fact of the matter is that lenders of all shapes and sizes have the choice as to which of the major agencies they report their loan performance data to; some chose just one CRA whilst others choose two or even all three. So, despite the degree of data sharing that exists within the CRA industry, as many people will recognise, the information on your credit file from one CRA can be materially different from that shown on another CRA.
This phenomena, known as “thin files” drives up the costs for lenders because it leads to an increase in declined applications or referrals (those applicants needing more labour-intensive underwriting attention) which means a drop in conversion rates and, more importantly, disappointed applicants. CRAs do also have occasional outages which leads to the risk of yet more declines and referrals, because applicants cannot be searched for at all during these times.
No wonder, then, that the prospect of lenders using more than one CRA to assess an applicant -known as the multi-bureau approach – has gained popularity in recent years. What better way to overcome the “thin file” problem, than by calling a second or even third CRA to get a full picture of your customer? It’s a treasure trove of valuable information when the decision is in the balance! But, just like Indiana Jones trying to find his way to the Holy Grail, metaphorical spears emerge from the cave walls and floors collapse beneath him. In less dramatic terms, there are some significant obstacles enroute to reaping the benefits of a multi-bureau strategy, and traps which are all too easy to fall into.
The most significant of these challenges is the double counting trap. In 2020, the major CRAs each reported coverage upwards of 70% of UK adults, which leads to two conclusions:
- Some CRAs have limited or no visibility on up to 30% of the UK adult population (for some CRAs this number will be materially lower)
- As there are three major CRAs, there will inevitably be duplication, after all, 3 x 70% equals 210% coverage!
So, to the uninitiated, a multi-bureau approach would begin with a lender searching one bureau, resulting in a “thin file”, before searching a second bureau to augment the data already obtained. The additional data shows more credit, but also restates some credit commitments from the first search, resulting in enough data to make a lending decision BUT duplicated credit commitments, which can potentially destroy the affordability calculation. The confusion and lack of clarity creates the need for a manual review or automatic decline, leading to higher costs and low conversions.
What’s more, one of the biggest risks associated with this approach is of failing to Treat Customers Fairly, a giant boulder thundering towards lenders whose only option is to try to outrun it, because whilst it is well accepted that customers should not be granted credit they cannot afford, it is also unfair to evaluate applicants based upon faulty affordability assessments.
Let us say that the first CRA returns details of a mortgage, two credit cards and two personal loans, but a second CRA returns the same mortgage, one credit card (also included in the first CRA search) and two personal loans (only one of which was included in the first CRA search).
The applicant therefore has one mortgage, two credit cards and a total of three personal loans, however, the combined searches show three credit cards and four personal loans. Unless this information is subjected to sophisticated analysis and robust deduplication, a lender may inadvertently double count the offending duplicated commitments and fail the affordability calculation, thus declining the application.
This is just one of thousands of examples of how multi-bureau strategies can deliver better underwriting decisions if used effectively but can also create unintended consequences if not, and it is one of the many ways in which DeeJoop, the revolutionary new SaaS service from LendingMetrics can help.
Using a sophisticated algorithmic approach, DeeJoop analyses multi-bureau API files in real time and returns to the lender a consolidated and distilled view, thus eliminating the risk of double counting credit commitments. This in turn allows for a much higher applicant conversion rate and a more accurate automated affordability calculation and credit decision; a Holy Grail when it comes to onboarding.
Such accuracy gives lenders the confidence that they are making high quality, high velocity and high-volume decisions, as cost effectively as possible, and Treating Customers Fairly whilst doing so.