Credit Scores

Have you ever tried checking your credit score online with different providers?

By Paul Lehair

I had previously checked my credit score anecdotally but when I did it again recently on different credit reference agencies (CRA) and price comparison websites, I was shocked to see the extent by which my score varied. Maybe I was unlucky, but my credit score ranged from close to 100% perfect (‘excellent’) to simply ‘good’ to being in the UK average (‘fair’). More frustrating was the fact that no clear explanation was given to explain the scores besides some very generic information that was not helpful.
The data behind these various scores ultimately comes from one of the 3 traditional CRA we have in the UK (Equifax, Experian, TransUnion). The inconsistency and lack of transparency revealed wider issues with existing credit scores. The data (and resulting score) used by the incumbent CRA is:

1. incomplete as they mainly rely on credit repayment and some basic income data but do not use any expenditure data;
2. static and outdated as there is typically a time lag of several months for them to receive it. The fact that this data is not real time is even more of an issue in an economic crisis like the one we are currently facing;
3. non-existent if, for instance, you have not borrowed before or recently moved to the country;
4. opaque as we have seen from the example above.

As a result, lenders using this data to issue credit make imperfect decisions leading to many applications getting wrongly declined (false negatives) but also wrongly accepted (false positives). This unfortunately also leads to many consumers (up to 9m in the UK) being excluded from mainstream credit because of non-existent or ‘thin’ credit files. This is becoming an even bigger issue with the ‘gig economy’ as lenders struggle to assess the affordability and risk of applicants who work as contractors, freelancers or on zero-hour contracts.

A massive market facing regulatory pressures (and opportunities) 

We all apply for credit numerous times throughout our lives whether to purchase assets (cars, flats, etc.) or to finance general expenditure (credit cards, personal loans etc.) so it is evident that this is a massive market. Over £100bn of new consumer credit (~£350bn including mortgages) is issued every year through ~4,000 lenders in the UK alone. The 3 incumbent CRAs also generate over £1bn of revenue in this country.
Understandably, this market is under close scrutiny from the regulator and various regulatory initiatives have been further catalysts for change.
A few years ago, the Financial Conduct Authority (FCA) introduced stricter affordability assessments for lenders, requiring them to now also assess affordability to the borrower in addition to credit risk to the lender. The incumbent CRAs are unable to assist with this due to their lack of expenditure data meaning lenders have to go through very manual processes to check bank statements of applicants (and a bad user experience for borrowers).
In addition, the FCA began a review of the UK credit information market in 2019 as it “identified concerns about the coverage and quality of credit information, the effectiveness of competition between CRAs, and the extent of consumer engagement”, which is further evidence of issues with the incumbent CRAs. 

Finally, the advent of Open Banking legislation in 2018, which forced banks in Europe to open up access to customers’ bank accounts (subject to consent), has enabled third-parties, in particular new start-ups, to access the granular account information enabling them to develop new applications and software.

CREDIT KUDOS = the power of open banking + machine learning

Credit Kudos uses open banking to access consumers’ full bank transactions data. Contrary to the issues described previously, this granular data is comprehensive (as it details all income and expenditure), real-time, up-to-date and can even be used for consumers with no credit history.
Open banking is great but not enough so one of the things that really excite us about Credit Kudos is that it provides a full-stack data analytics solution aggregating data, categorising it and analysing it. Indeed, Credit Kudos has built several layers of proprietary tech including an industry-leading categorisation engine and predictive algorithms using machine learning.
Ultimately, the Credit Kudos platform goes beyond traditional scoring and gives a full picture of the affordability and risk profiles of applicants to lenders allowing them to:

1. make faster decisions by automating affordability assessments and replacing highly manual processes;
2. increase acceptance rates by reducing false negatives and previously overlooked customers;
3. reduce defaults thanks to better predictions.

The incredible traction they experienced is a testament to the quality of their product: Credit Kudos on-boarded over 50 new lenders last year.

Product-obsessed team on a mission
Credit Kudos Team

All of this is of course the result of the amazing people behind Credit Kudos. The team is highly technical, being led by two Cofounders that are both engineers: Freddy Kelly (CEO) and Matt Schofield (CTO) who met at university studying computer science and later joined the 2015/2016 Entrepreneur First cohort. We were impressed by their ambition and persistence but also their humility, maturity and the impressive team they have brought into the business, not least Kelly Read-Parish
(COO).
We also loved their obsession with building an amazing product and user experience. Their clients praise the product and the team for their responsiveness, and word spreads fast in the lending industry!
Last but not least, we really admire their wider mission which aims to advance financial inclusion through new applications of technology. Credit Kudos has the potential to have great social and economic impacts by helping people trapped in poverty premiums and high-cost credit options. By enabling lenders to make better decisions, they simultaneously allow previously overlooked individuals access mainstream credit.

Our investment in Credit Kudos reflects  AlbionVC’s focus on building category-leading software companies that are leveraging new technologies and machine learning to disrupt sectors crying out for change. It follows our other investments into data analytics and AI/ML companies including Avora, Black Swan Data, Concirrus, Elliptic, Hazy, Imandra, Panaseer, Phrasee, Quantexa and Speechmatics.
We are very happy that Credit Kudos chose AlbionVC to lead their series A and could not be more excited to join them along their journey.

Image credit: Minh Uong, The New York Times