Three credit cards in background, text over foreground.

Industry Insider Shows How Big Data Doesn’t Help Consumers

Shortly after you are laid off data services will see triggers like, #OpenToWork on LinkedIn. Other triggers include defaults, late payments, or change in employment on your credit score.

Credit reporting agencies sells this data to banks, if the banks don't buy similar details from data compilers. A model risk is created which these triggers reduce credit lines without review.

Banks and credit card issuers are getting lazy. The belief is that big data is accurate, predictive, and safe enough to use in automation. This verse personal banking decisions made on a case by case basis.

Lazy Banks Firm Find Gold Mine In Big Data

From the banks prospective a change in employment plus lower credit score means more risk. Accounts should be reevaluated. Yet, it is much more profitable to cut credit limits to above balance.

Reducing credit limit to a balance level has an informal title of “fee farming.” This is because such cut will most likely trigger an overdraft or exceeding the balance.

At that point a fee can be charged. Then according to the credit agreement, interest rates can be increased. The consumer will either have to pay off the balance, or struggle to make payments.

The use of big data gives a ready excuse for the reduction in rates. The increase in fees is disconnected with the action. Over time justification for lowering the limit gains confirmation because the a percentage of consumers becomes delinquent.

Of those, many their situation doesn't improve. Yet the majority overdraft immediately because the lower credit limit. Going over the limit feeds a signal to the credit reporting agency.

Additional Risk With Big Financial Data Compounds

That over limit signal compounds the problem with other credit cards the consumer has. Whether credit cards are on time. Big data decisions without oversight create systemic problems.

What increases the risk for the bank is when geographic, demographic, and psychographic data is appended. For example, decisions that contain ethic, race, gender, or non-financial factors.

Rather than assessing individual risk, banks are reducing credit limits for all consumers flagged by this data. That's easy, low cost, and justifiable. The overhead of sorting out accounts goes away with automation.

It's not ethical, because the cause “lowering credit limit” creates an effect “over limit fees and higher interest rates.” While the cause and effect are spread out, a join of the data shows heavy overlap.

Being between jobs, the consumer is in the awkward position of not having a float on the credit card. Such as paying a least amount. The programmatic decision harms the banks reputation, but pays well.

An “credit card over limit fee” might only be $25, but multiple that by the 23 million job losses over COVID alone. Consumers will struggle to pay back balances, fees, and interest on those fees. It's a billion dollar oversight.

Your Misfortune Becomes A Record Year For Banking

No wonder financial services firms have record profits during economic downturns. With many living paycheck to paycheck, there is no incentive for banks to do anything but fee farming.

It's very profitable and easy to hide. Outsiders miss that banks are creating money charging fees on accounts. Automating those fees with plausible data triggers eliminates the need for direct oversight.

Today having a credit card is required. Cash is being made more inconvenient everyday because credit cards are profitable. If you haven't noticed, banks are reducing the number of mortgagees and business credit lines they issue.

That's because a 29% interest rate on a credit card is easy to charge and consumers accept it. As long as consumers are paying something, the bank can multiple the remaining balance with fees. Accounts create their own transactional evidence of a at risk consumer.

What Consumers Can Do If Victim Of Fee Farming

An appeal of the credit decision isn't in the consumers interest because the harm is already done. If this happens to you, immediately file a complaint with the Consumer Financial Protection Bureau because it is unethical behavior.

No new regulation is necessary. Internal compliance and audit groups can find these risks if they aren't chasing fires. Regulators using modern sampling methods and data science will find these patterns easy.

Using the same big data will find an uptick in fees collected after credit line reductions. If the individuals who have increasing fees match those who have had limits reduced, then evidence speaks to fee farming.

What Banks Can Do To Reduce Model And Reputation Risk

Of course, there are legitimate risk conditions that make it necessary to reduce a consumers credit limit. The concern here is the programmatic reduction and then not equipping customer service with reason why.

Reducing a consumers credit limit to slightly over their credit balance is a red flag as well.

If your bank is doing this, you can reduce the model and reputation risk by checking recommendations against in-house qualifications. Provide written notification BEFORE lowering the credit limit. Don't let a dataset make the final decision.

When reducing the credit limit, cut it in half but over the balance to allow the consumer head room. It won't be as profitable as farming fees, but it will buy you some loyalty from the consumer. Triage your customers to find those with high future value.

The customer you upset today with fee farming, could be a mortgage or future credit holder in the future. No short term gain is worth a getting caught running these unwritten schemes.

“Fee Farming” is a deliberate activity. It's designed to increase fees and interest rates within the terms of a credit card. The use of data and automated triggers makes it seem fair, but implementation shows otherwise.

Big Data Offers Great Power And Responsibility

When big data offers you more profits, understand it works both ways. Enough consumers raising a concern about fee farming will make it difficult to hide. Especially since that same data can show the snowball of fees relative to the reduction in credit limit.

This is a different story if the consumer misses a payment with your organization. I hope I've been fair with both sides of this sensitive subject. Banks don't want you to know this exists because it is so profitable they aren't willing to fix it.

I've written a lot of on debt management from the high-income professional prospective, as well as credit risk from the banks prospective. Ask your questions, I'd like to hear if this has happened to you, or what is best practice to prevent risk in this area.