Match Factor: Smarter Credit Comparison

I’m excited to be able to announce a first for the credit industry: intelligent search results ranking for credit products.

TotallyMoney.com Match Factor performs a series of complex calculations and analyses of eligibility; APR and other product features; lender acceptance data; and usability and user experience insights.

These algorithms are personalised to reveal the best credit product for each customer at the top of their personalised table of search results.

MatchFactor

Match Factor is an evolution of the eligibility check; it’s the first of many new initiatives that we are launching this year to make the credit market fairer for customers.

Match Factor will mean that more people will be happier with the credit for which they apply and are accepted.

TotallyMoney.com Match Factor essentials

  • Simple: Makes it easy for the consumer to find the best credit product;
  • Transparent: Provides a 360° view of product suitability;
  • Personalised: Individual ranking for each consumer;
  • Unbiased: Lenders cannot pay to improve their Match Factor ranking;
  • Advanced: Rapid algorithmic analysis of multiple factors;
  • Evolving: Will harness machine learning to optimise against the products that consumers are looking for and selecting.

Market makers

TotallyMoney.com was the first to offer pre-application credit eligibility checks with the Fluid credit card and they have been trailblazers for eligibility matching ever since.

Being able to see your likelihood of acceptance before you applied for credit was an important step forward in readdressing the balance between consumer and lender.

Historically, consumers were flying blind; attracted by lenders’ advertising they would have to complete an application and wait to see if they’d be accepted. With each application, a full search would be recorded on their credit file.

Each search would be visible to other lenders and too many in quick succession would raise a warning flag that the customer was desperate for money. It could look as though they were being rejected again and again and would not be considered a good credit risk; each mark made it progressively harder for the consumer to get credit.

Eligibility pioneers

The pre-application eligibility check pioneered by TotallyMoney.com uses a soft search of the consumer’s credit file, often called a quotation search. Unlike the full credit search, this isn’t visible to other lenders so has no impact on the consumer’s ability to get credit in the future.

Today, almost half a million new customers every month use the TotallyMoney.com eligibility matching service – and those who check their eligibility before applying are 69% more likely to be approved for credit.[1]

Eligibility checks have given users a valuable indication of how likely they are to be accepted for a loan, but they are not the whole story and increasingly paint a misleading picture of what’s best for the consumer.

One step ahead

Lenders can offset the risk of lowering the eligibility bar and accept more applications by offering loans with a higher rate of interest; the more likely they think it is that people will default and fail to repay the loan, the higher the interest rate they will set.

Lenders can then return eligibility scores that push these higher-rate loans to the top of comparison tables.

Consumers may find that they are offered an expensive loan for which they are only slightly more likely to be accepted than a much lower rate loan.

Consumers are left to weigh up the different factors themselves and given little help in finding the best loan.

Consumer first

Match Factor puts the consumer first and helps them quickly find the best credit product for them. It is available on the TotallyMoney.com loans comparison platform at the moment; try it for yourself.

Match Factor challenges lenders to improve their products; the only way that they can move up the table is to provide consumers with better value credit and an improved application experience.

[1] Based on visits to TotallyMoney.com, Oct-Dec 2015.

   

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