Banks Move Credit Decisions to the Transaction Level

A lender evaluated a borrower, assigned a limit and established terms. The customer then spent against that pool of credit across hundreds or thousands of purchases. Outside of asset-linked products such as mortgages, the original underwriting decision may have little connection to what the borrowed money ultimately finances.

But findings in the latest installment of The Future of AI Credit, a PYMNTS Intelligence collaboration with Thredd, reveal how that historical separation is beginning to dissolve as tokenization gives an individual transaction an addressable identity, real-time payment data supplies context about the purchase and artificial intelligence turns that context into a decision at transaction speed.

The important distinction is that AI is not simply automating conventional underwriting. It is helping create a new decisioning layer around the transaction itself.

Underwriting Is Only the First Decision of Many in the Financial Relationship

Traditional underwriting asks whether a customer qualifies for credit and on what terms. It is generally conducted at origination using the information available at that point. Transaction decisioning asks a different question: How should this particular purchase be treated?

The answer to that new question is reshaping both the mechanics of credit and the basis of competition among issuers. A transaction might be approved against an existing revolving line, routed to another funding source, converted into installments or subjected to an additional control.

Underwriting may happen once; transaction decisioning must happen every time a customer spends. That distinction exposes a limitation in the conventional credit model. Most consumer and small business credit is still priced primarily according to the amount borrowed and the borrower’s general risk profile. Yet two purchases of the same value can have different underlying economics.

The report illustrated the point with a $5,000 hotel expense and $5,000 of office furniture. The first has little resale value. The second is an asset that could potentially be sold. The amount is identical, but the risk profile is not. Transaction data can help lenders distinguish between those cases at the moment of purchase.

See the report: Credit as a Feature: How AI Turns Credit From a Product Into a Moment

AI’s role is therefore broader than making a credit score more accurate. It can help identify what is being purchased, incorporate more current signals than a static credit file and determine how a specific transaction should be handled in context.

Issuers who once differentiated credit products through APRs, rewards, promotional offers and credit limits may find those features are no longer sufficient to fend off competition. In a transaction-level model, competitive advantage increasingly depends on infrastructure: the quality of the issuer’s data, the adaptability of its processor, the speed of its decisioning engine and its ability to orchestrate several funding options without disrupting the payment experience.

Nearly half of Gen Z consumers say they are increasing their use of credit specifically as a tool for managing spending, according to findings cited in the report. Gen Z cardholders also express demand for greater control over how and when they repay, and they enable credit-account push notifications at higher rates than older consumers.

That points to a generation that may judge credit less by the size of a static line and more by how effectively it helps manage cash flow in real time. The report explicitly rejected the idea that the opportunity is simply to produce slightly fewer declines or marginally better pricing on the same static credit line. The larger opportunity is to deliver a different product that appears in the right form at the right moment for the transaction at hand.