Enabling low cost loans


My hoover’s just broke, the iron’s just broke,
so I need stuff for the house ’cause everything’s broken. And I also owe somebody for rent.
It’s my birthday as well today. I’m paying off debts on my birthday, I’m getting a loan
on my birthday, you know what I mean? It’s nice. Moneyline started about 10 years ago to tackle
financial exclusion in a particular borough. A typical customer would generally be a young
woman living in social housing, and will have multiple dependence and basically living wholly
or partially on benefit, and managing their finances largely week to week. We got involved with the University of Salford
because we already had quite a long-standing relationship with Professor Karl Dayson who
worked in financial inclusion. He suggested we meet up with the School of Computing and
Science, because they were looking to run what’s called a Knowledge Transfer Partnership. We knew we had this kind of bank of data that
we collected over a period of time for all our customers, and we wanted to put it to
some use to try and analyse – were there any patterns in that data that might predict the
risk of lending to certain types of customers? I work mainly in the field of data mining.
Data mining is a field where you look at vast volumes of data, and try and extract patterns
and decision-making models. There’s no size really in creating decision
models for this particular group of people. What you have to do is discover the knowledge
that minimizes the risk and enables the organisation to lend in a sensible, ethical way. Left work about five years ago to look after
my kids, and after that it started getting rather difficult to manage with the money.
Three boys — seven, five, and one and half — they’re quite expensive with the clothes
and the shoes. The first time I came in it was to fix up
my new house. Since then I’ve been in and had a few more loans to help me out with Christmas
and like birthdays. An ordinary bank, in terms of how they assess
a customer, will look at traditional credit scoring — which will look at credit card
behaviour, which will look at different loans and repayments on loans – where our customers
are largely operating outside of that. They’re mostly dealing in cash, and they’re mostly
not really making full use of a bank account, because they only really have access to a
basic bank account. A lot of people have got themselves into a
lot of difficulty out there, from doorstep lenders and the high street lenders, and the
typical customer’s coming in with loans outstanding of 500 pounds. Average interest on that is
approximately about 80 pounds per 100. And then we do get the older generation as
well, who tend to obviously renew the loans one after another. A lot of the older generation
have around about six or seven loans with us. And what’s the reason for the loan? For a baby; baby and a trip for my son. He’s
going away next week — Prestatyn, with the school. I’ve already paid for the trip, but
this is just a few bits that he’s gathering to go away with. What we’ve managed to do is to develop a microfinance
model which can be used for small lending. The work at Salford focus on something called
cost-sensitive learning that takes account of cost and risk as well as accuracy. The main benefit is that they’re able to help
people who otherwise won’t get loans. It would be impossible to allow them to grant loans
on the risk levels that are perceived. In actual fact, the risk levels are significantly
reduced if you use data mining to produce the models, and then apply the data mining
models to make a decision. The research has helped us to actually have
the confidence to expand into more geographical locations. We ultimately want to make the
business national, and it means more people who wouldn’t necessarily have had access to
the service will have access to affordable finance. One of the big benefits for the University
has been that it’s brought together people from the social scientist side and the computer
science side in a multi-disciplinary project – taking account of social factors as well
as hard, objective factors, and providing solutions that a single discipline on its
own would have been unable to provide. It’s access to knowledge that we wouldn’t
have internally, and we can use that knowledge to develop the business. I just need you to sign and date the application
form for me please? A lot of them, they’re extortionist, aren’t
they mate? They charge thousands, like 999 per cent. It’s ridiculous, they shouldn’t
be allowed to do that. We are really pleased that the models we’ve
developed has enabled a more consistent approach to lending, in a way that helps people — people
who not normally get loans from banks, people who probably need the money the most, and
people who would otherwise struggle. I don’t know where to be without you, to be
honest with you. You’ve been a godsend.

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