Credit, and the consumer’s relationship with it, has changed rather dramatically over time. But those changes can only be seen if one takes a big step back and looks across the intersection of credit products, credit usage and consumer behavior. Fifty years ago, when customers thought about using credit, it was in the context of buying something expensive that they really needed — a refrigerator, a washer/dryer, a car — with the intention to pay off that loan balance quickly.
But using credit cards as a form of currency, even as recently as twenty years ago — David Rosenberg, founder, chairman and CEO of Unifund Group and Unirush Rushcard told Karen Webster in a recent conversation — was still relatively rare.
“In the last 20 or 30 years, I am sure we have both known people who’ve said pretty forcefully, ‘I will never ever use a credit card to buy my groceries. It is against my morals.’”
That sort of a response now, Rosenberg noted, seems a bit strange, because how consumers use cards of all kinds — credit, prepaid, debit, etc — has evolved very significantly.
“Today, one would say, ‘What do you mean you don’t believe in this?’ Using credit isn’t a religion — it’s a bookkeeping service and a method of payment,” Rosenberg emphasized. “The customer might be getting frequent flyer points when they use the card and are perfectly capable of paying back the debt, but [the frequent flyer points] incentive moves them to use a method of payment that isn’t cash, and isn’t a debit card.”
A method of payment, he said, that makes very good sense for consumers who pay their card balances in full every month, because those incentives amount to being offered the opportunity to conduct commerce at a discount.
Credit as an obligation — something you used when you needed to or very bad things would happen — is something of an outdated use model, Rosenberg said.
But for all that evolution in credit — and how consumers tend to view it and use it — how consumers actually think about the subject is fairly homogenized, Rosenberg explained, because “we get lost in the averages.”
“Everything is either as good as it looks, or everything is not as good as it looks — those are both true, at all times,” he said. “They just happen to affect different portions and segments of consumers. And paying attention to those consumer segments and what they are doing gives us a better indication of how the people are doing themselves, how credit is doing and how the business of credit is doing.”
That more homogenous system, Rosenberg told Webster, is something of a natural outcome of how consumers have shifted their commerce and credit relationships in a way that has become a largely impersonal experience between a consumer and a third-party — the issuer — rather than the merchant from which they are buying.
Because, as it turns out, using credit and using it to its best advantage isn’t the same for all consumers. But all consumers — no matter when they are on the credit spectrum — can benefit from a system that more individually understands the use and need profiles of its participants.
Rosenberg says that the future is about the use of artificial intelligence (AI) and machine learning in real time, when the issuer of the credit is able to look at thousands of variables and target an offer to circumstances of an individual, rather than “a homogenized factory process that generically issues rewards.”
Credit as a Currency
For super-prime customers — those for whom credit cards are basically payment cards because those consumers pay them in full monthly and never accrue any interest — Rosenberg said that card “borrowing” is an incentive-driven system. The customer gets cash back, rewards points, airline miles, hotel stays — and those incentives have very successfully attracted consumers to use them to buy things. Rosenberg noted that for a large segment of consumers, buying something without using a credit card is almost a foreign concept because the card offers them access to a more valuable form of currency.
Rosenberg has observed that the biggest changes in the use of credit have been in these groups of consumers — the incentives, in many ways, are geared toward them.
But a more artificial intelligence-driven, machine learning system, Rosenberg said, will allow those customers who are locked out of the credit market today to be seen more clearly by creditors, and to be given incentives that are more explicitly tailored to their wants.
A Better Subprime Experience
The problem with the categorizations of “prime” and “sub-prime” borrower is that they are somewhat less hard and fast than people like to think they are. Rosenberg explained that there aren’t simply two classes of buyers — those who pay their bills and those who don’t.
“There is a large floating population of people who will cross back and forth between those two categories depending on their circumstances,” Rosenberg said. “But we tend to want to place people into that category — people [who] don’t pay … [their] bills, so … [they] are a subprime borrower and people who do, so they are prime.”
But, Rosenberg said, the truth is most people pay bills in the order of absolute necessity, and when things get bad financially because a job was lost or an economic catastrophe has happened, that order gets very utilitarian. Car payments get paid, because car companies have no sense of humor and will repossess a car. But eviction from a house takes longer, which means a person with a more pressing need would rather skip their mortgage for a few months than not buy medication they need. And credit card bills? Those quickly fall to the bottom of a pile.
“This is how consumers have been educated by banks and service providers,” Rosenberg told Webster.
The “bad thing” that’s going to happen though is that consumer credit scores get ruined. It can happen pretty fast, and without a good way to work their way through a system, those consumers get locked out. Rosenberg said that instead, consumers get a good education on how not to pay, because the customer hasn’t been given a reason to do so.
Instead, Rosenberg said, much the way you can with a prime credit customer, using intelligent machines means getting a better look at a consumer’s more specific story — and finding ways to tie incentives and products to those needs.
“[At Unifund] we have a no-touch policy, because calling someone who is in dire straights is pointless,” Rosenberg said, “Maybe you can … embarrass them and earn yourself a fine for violating consumer protection laws, but it won’t solve the problem. Sometimes the solution is wait six months, let them get on their feet and then connect them with an offer that speaks to their circumstances.”
Consumers of all stripes — prime, sub-prime and the great floating masses that exist somewhere in between those two poles — have need and use of credit products, Rosenberg noted, but they just don’t all need the same products.
And while the world will not get less large or digital, the tools of the digital world give the payments ecosystem as a whole a better set of tools to connect better credit experiences to the circumstances they will address.
“The future of credit is about tying what is offered to the right person for the right reason,” Rosenberg emphasized. “The more precise you can be about the how, where … and why of someone seeking credit, the better you can really tie that offer to what is going on in someone’s life — the better … the offer really is for all parties involved.”