Traditional Credit Scores Aren’t Dead Yet

2 Minutes Read

But the recent Equifax flub suggests they’re also not as bulletproof as they used to be

Recently, a surprising news report hit the internet.

A startling headline showed up on national news sites the afternoon of August 4: Equifax coding error caused millions of inaccurate credit scores. How to know if you are affected.

A problem like this, while unusual, isn’t unheard of.

FICO scores have long been the standard metric that loan underwriters use to assess the riskiness of a given loan. Yet people have also long suspected traditional credit scores may not tell the whole story of a prospective borrower’s ability to make on-time loan payments.

For starters, everyone who regularly works with large amounts of data knows that every once in a while, something will go seriously wrong. It comes with the territory.

But human error of the Equifax type, while it does happen, is only part of the story.

You can understand this better when you see the things credit scores measure.

The typical credit score measures five things:

  • Do you make loan payments on time?  35% of score
  • What’s your total outstanding loan and credit card balance?  30% of score
  • How long have you made loan or credit card payments?  15% of score
  • What mix of loans or credit cards do you have?  10 % of score
  • How long have you had loans or credit cards?  10% of score

This approach gives you a general overview of the likelihood of a person’s loan repayment.

However, there are a number of important real-life financial factors missing. Wouldn’t you also want to consider and analyze these if you could?

  • Employment information
    • Does the borrower have predictable regular income?
  • Bank statement data
    • Does the borrower pay rent, and do they make the rent payments on time?
    • Are they on-time with payments to utilities, cell carrier, streaming services, and the other recurring monthly bills?
  • These additional non-traditional data sets can also be analyzed
    • Mobile device data
    • Internet-of-Things (IoT) sensor data
      • This includes information from any web-enabled appliance or device
    • Social media posts
    • Web product reviews
    • Web usage and app usage

The idea behind the use of alternative data is that it enables lenders to look at a fuller, more complete financial “picture” of an individual. This, in turn, often leads to approved loans which would likely otherwise have been denied if traditional credit scores had been used exclusively.

Think about what this alternative data underwriting could mean to your members who have thin traditional credit scores or perhaps no credit score at all.

And this is where we come in.

Lokyata is an automated credit decisioning platform that can enable you to do these things.

Our technology makes it easier for you to use alternative data to assess borrower creditworthiness. It also allows you to be less dependent on traditional credit scores. You’ll be able to extend affordable loans to more members and do so more efficiently than you could in the past. Plus, you’ll consistently deliver better member experiences, and more of them.

 In simple terms, Lokyata improves your lending and helps your CU in at least four ways.

  1. Grows your loan portfolio without an equal increase in credit risk.
  2. Saves time and money from back-office inefficiencies and fewer high-risk credit reviews.
  3. Makes affordable credit available to more of your members.
  4. Helps build financial health and wellbeing for more individuals and families.

Register on the website today for a Lokyata demonstration.

See for yourself how it works and gain a greater appreciation for what Lokyata’s technology could do for your credit union and your members. In the meantime, if you'd like more information about Lokyata, you can visit or email J.D. Crouch,


Book a Demo With Lokyata

Book a Demo


Picture of Lokyata


Lokyata revolutionizes credit decisioning allowing consumer lenders to say "yes" to more applicants by configuring their criteria into decision workflows, deploying fraud detection measures, and replacing manual processes with automation.