The data we use for lending decisions in the UK is out of sync with modern realities.
Such a narrow approach financially excludes people on higher incomes, losing lenders valuable income.
It is ironic that the most digitally included are becoming the new financially excluded.
Credit reference agencies scoring systems tend to dislike tenants, especially those who move around often which is the norm in the UK market where prices are increasing and the average tenancy is just six months long.
High street banks’ risk models refuse credit to applicants who have had to resort to subprime short-term high cost credit, a self-perpetuating cycle as borrowers return to expensive lenders when they have nowhere else to go. Self-employment or a regular change of job drives a score down further.
The solution to this modern day problem can be found in the digitalisation of bygone age. An age where the local bank manager knew you personally.
The UK Chancellor of the Exchequer, George Osborne has declared his desire to return to a Dad’s Army style of banking. This was when “the bank manager was at the very centre of local life, knew all the businesses, knew the people who ran the businesses and was empowered to make judgements.”
These all-seeing, all-knowing bank managers couldn’t keep pace with demand which saw UK consumers increase their borrowing from £200 billion to £1.4 trillion in the twenty years to 2010. And nor did they have many places left to ‘see the whites of borrowers’ eyes’. 600 bank branches closed in 2015 alone.
Automated decisions replaced the human touch as rule based (and therefore simplistic) algorithms extend £300m of credit every day in the UK.
Automated systems can manage four of the five C’s of lending:
- Capacity – the borrower can afford the loan
- Capital – the borrower’s assets
- Collateral – security for the loan
- Conditions – interest rates etc can be assessed using automated systems
- Character – is much harder. Surely algorithms can’t tell if you intend to repay a loan like the bank manager was fairly adept at doing?
Whereas the bank manager of olde may have known your social circle, Facebook can show who you’re connected with using their social graph.
A review of LinkedIn can provide details of your job using their professional graph. Granting temporary access your banking app can show you pay your bills on time.
If your mobile device shows that you call 10 people on a regular basis, this can show stability of relationships. Combining social, professional and financial data sets will give the financial service provider a much more accurate picture of the borrower.
These factors should contribute to your credit score; they are more relevant as data sets, especially for younger private tenants who move jobs and location often, sometimes traveling abroad – those who are more likely to have ‘thin files’.
The issue of thin (or no) files is more acute in the Global South where fewer than one in ten people are accessing mainstream credit.
But there are other ways to assess someone’s trustworthiness through the digitalisation of the local bank manager.
The following Ted Talk by Shivani Siroya demonstrates a practical application of the ideas explored above in support of some of the world’s poorest communities:
Here at MoneyCircles we help potential members connect to financial co-operatives through social media. By using some of the ideas shown above our platform can simplify on-boarding and data can be provided to help financial institutions know their customer and assess credit risk.
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