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How the ‘Invisible’ Population can be Seen: Using Alternative Data to Help SME Funding

Access to finance is a cornerstone of small businesses’ survival – and with 6 million SMEs in the UK supporting the backbone of the economy it is important to address the lack of funding currently available. 

Mikkel Velin co-CEO of YouLend shares how alternative data can help to improve funding to SMEs throughout the UK

Youlend
Mikkel Velin, Co-CEO of YouLend

There remains a significant demand for finance amongst SMEs, over one-quarter of firms in the UK shut down every year due to lack of funding. Although the growth of SMEs in the UK market has far outpaced the growth of larger businesses, gross lending flows to SMEs by banks have remained flat over the past decade

Availability of credit for SMEs is clearly a major issue for their own health and the broader economy. Traditional sources of funding, model a strong dependence on the proprietor’s credit history but this leaves out 5.8 million* adults deemed ‘invisible’ by traditional lending structures. These include:

  • Younger people with a limited established credit record
  • Older people who may have paid off their mortgage or not relied on credit products
  • Unbanked who are often credit invisible
  • Recent immigrants with no credit footprint

Opportunity for Alternative Lenders

Consumers with thin or non-existent credit files provide an opportunity for lenders to use alternative data to make credit decisions. Access to enhanced information (alternative data) would also increase available funding options to those marginally declined – people narrowly rejected for a credit card or personal loan, but who are unlikely to default. 

‘Alternative’ data refers broadly to any information that has not historically been part of credit reports. Examples of this include non-credit payment behaviours of customers, as well as metrics around the perception of the SMEs amongst their customer bases.

The biggest consumer reporting agencies have realised the importance of non-credit payments in assessing the full profile of customers. For instance, Experian have started advertising their ‘Boost’ service that allows customers to share repayment history for expenses, like utilities and monthly council tax bills, in order to boost credit bureau scores. 

Another example is in the US where FICO has introduced ‘FICO Score XD’ in partnership with LexisNexis Risk Solutions and Equifax to serve the credit-invisible consumers by including utility, phone, and TV bill payment data in their reports. Currently, traditional credit bureaus could only see if someone had unpaid utility or phone bills that have been turned over to collections agencies. But with consumer-permissioned data, a consumer could permit financing providers to access their record of paying bills on time. Instead of only being penalised for missed payments, a consumer could get rewarded for on-time payments.

This shift indicates that the industry has crossed the bridge to include the ‘invisible’ population in the traditional credit market. However alternative data can go above and beyond mere payment histories.

Alternative data leveraged 

Credit risk modelling and alternative data sources needed to improve underwriting speed, accuracy, and outcome. By relying exclusively on credit reports and traditional risk modelling techniques, many financing providers ignore some of the best risk predictors in an increasingly digital world of SMEs. 

It is now possible to generate deeper insights into customer behaviours of funding applicants. Alternative data sources and algorithms can identify customer concentration risks, average transaction sizes, repeat customer purchase behaviours at the merchant site that serve as powerful predictors informing of the creditworthiness of the business. It is no longer a case of assessing merely the probability of a merchant not repaying, the better approach is assessing the risk of a merchant losing their customer base that drives those repayments. 

By complementing (and not replacing) traditional underwriting parameters with others that are correlated with affordability and propensity to repay financing, this creates a much fairer and quantifiable approach to understanding all SMEs creditworthiness: including those with a credit history and those without it; as well as those with a brick-and-mortar presence, and those with only an online presence. Using alternative data sources will open up opportunities for businesses previously excluded from the finance market.  

Author

  • Polly is a journalist, content creator and general opinion holder from North Wales. She has written for a number of publications, usually hovering around the topics of fintech, tech, lifestyle and body positivity.

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