How Transaction Data Can Expand Credit Access

TLDR

Transaction data can help lenders assess people and microenterprises that lack extensive credit histories. Bank deposits, mobile-wallet payments, point-of-sale sales and platform earnings can reveal cash-flow patterns that a conventional credit file misses. However, better repayment prediction is not the same as fair or beneficial lending. Responsible transaction data credit access also requires relevant data, meaningful consent, affordability checks, understandable decisions, error correction and a way to appeal.

In this article, transaction data credit access primarily means using payment and account records to assess a credit application. The phrase can also refer to a borrower’s ability to obtain and share those records through open-finance or data-portability systems. The two ideas are connected: transaction-based underwriting cannot work responsibly unless customers can understand and exercise appropriate control over how their records are accessed.

What counts as transaction data?

Transaction data record money moving into, out of or through an account or commercial system. Common sources include bank accounts, mobile-money accounts, digital wallets, card terminals, merchant point-of-sale systems, online marketplaces and work platforms. The IFC identifies mobile money, digital wallets, point-of-sale systems and bank statements among the inputs used in nontraditional credit-scoring models, while emphasizing that models and inclusion outcomes differ across markets, products and institutions.

These records are narrower than the broad category of “alternative data.” Device details, contact lists, location histories and social-media activity may also be labeled alternative data, but they are not transaction records. That distinction matters because information directly connected to revenue, expenses or repayment capacity is generally easier to justify than an expansive collection of personal behavior.

Data source	What it may reveal	What it may miss	Protection question
Mobile-money or wallet records	Customer payments, transfers, account activity and recurring inflows	Cash sales, activity in another wallet and shared-phone transactions	Is access limited to relevant transactions and a stated period?
Point-of-sale system	Sales frequency, ticket sizes, refunds and seasonal patterns	Off-system cash sales, informal expenses and unpaid invoices	Can the merchant inspect and correct the record?
Bank statements	Deposits, withdrawals, transfers, balances and payment timing	Money held outside the bank or business and household funds mixed together	Which accounts and transaction categories will the lender use?
Marketplace account	Sales, refunds, order frequency and performance on that marketplace	Offline trade and sales made through competing platforms	Can the borrower move or verify the history outside the platform?
Platform earnings	Work frequency, gross earnings and changes over time	Unpaid work, expenses and income earned through other channels	Does the assessment account for costs and income volatility?
Recurring bills	Regular payment behavior and continuing obligations	Complete income, informal debts and sudden household expenses	Is the bill record accurate, relevant and used with permission?

How transaction data credit access works

A lender can use transaction records for cash-flow underwriting: an assessment of whether observed inflows and outflows appear sufficient to support a proposed repayment schedule. For a small shop, the lender might examine recurring sales, supplier payments, refunds, balance volatility and seasonal slowdowns. For a platform worker, it might consider the frequency and variability of earnings.

This can help when the applicant has a thin conventional credit file, limited collateral or financial statements that do not fully capture day-to-day activity. A series of modest but regular deposits may make economic activity visible. Transaction data can also help distinguish a temporary dip from a recurring cash-flow problem—provided the history covers an appropriate period and the lender understands seasonality.

The assessment should reflect net repayment capacity, not merely gross receipts. A merchant with strong sales may also face high inventory costs, rent and household withdrawals. A loan that appears manageable against revenue alone may put restocking cash under pressure. Borrowers facing that problem can use a basic costing process before applying, such as this guide to pricing products when cash flow is tight.

World Bank digital-finance material describes lending through marketplaces and point-of-sale providers that may consider merchant profiles, sales, refunds, popularity and transaction-level information. Such systems can reduce the paperwork needed to document activity, but convenience should not be confused with suitability. Rapid approval can still produce a poorly sized loan or a repayment schedule that conflicts with the business cycle.

Who may benefit—and who may disappear from the data

Transaction-based assessment may be useful for digitally active merchants, platform workers and applicants whose formal credit histories are too limited to support a conventional score. It may also help established microenterprises that have real turnover but lack audited accounts or conventional collateral.

The same model can underserve people whose economic lives are not captured digitally. Cash-reliant merchants, workers with intermittent connectivity, businesses using shared accounts and sellers active across several platforms may present incomplete records. A low volume of visible transactions does not necessarily mean low economic activity or unwillingness to repay.

Data-dependent lending can therefore move the inclusion boundary rather than eliminate it. It may recognize some borrowers previously overlooked by conventional files while creating a new disadvantage for people without the right digital footprint. Lenders need ways to accept supplementary evidence and conduct a human review instead of treating missing data as evidence of excessive risk.

What the evidence actually shows

A useful example comes from CGAP research on transaction data for micro and small enterprise lending. Published in March 2024, the research examined two lending cases in India: one serving small shops and another serving platform workers. In those provider datasets, transaction-data scorecards predicted repayment. Combining transaction data with credit-history data improved prediction relative to using either source alone.

That is evidence for predictive value in two specific cases—not proof that transaction-data lending universally increases approvals, improves business income or strengthens long-term financial inclusion. Better prediction can help a lender distinguish risk, but the resulting decision could be an approval, a rejection, a smaller offer or different terms.

CGAP also reports research involving 852 customers of Fundfina, one of the providers studied. Among those customers, 62% said they had previously lacked access to the type of loan Fundfina offered. This customer-reported finding is relevant to access in that provider context, but it should not be generalized to all transaction-based lenders or markets.

More broadly, the effects of credit should be assessed through outcomes such as productive investment, liquidity, resilience and borrower stress—not approval volume alone. The same caution applies when evaluating microfinance and business growth evidence: outcomes can vary by borrower, product, use of funds and time horizon.

Can transaction records replace a credit file?

Usually, they are better treated as a complement rather than a universal replacement. The Indian cases offer one practical reason: combining transaction information with credit-history data produced stronger repayment prediction than either source alone in those datasets. Other information may still be needed to understand existing debts, irregular expenses, business costs and whether a proposed loan is affordable.

A transaction history is also a partial view. Bank records can omit cash trade; marketplace data can omit offline sales; platform earnings can omit work completed elsewhere. Even a technically accurate record may be misinterpreted if a model mistakes seasonality for deterioration or treats transfers between a person’s own accounts as income.

The main risks are not only about privacy

Consent can be formal without being meaningful

Applicants may click an authorization because credit is urgent without understanding its scope, duration or recipients. The World Bank’s Global Financial Inclusion and Consumer Protection Survey, which covers responses from financial-sector authorities in 130 economies, highlights informed consent as an important issue when alternative data are used in creditworthiness assessments.

Incorrect or incomplete records can affect a decision

Accounts can be wrongly linked, transactions can be misclassified and shared accounts can blend several people’s activity. In the United States, the Consumer Financial Protection Bureau has identified deposits, withdrawals and transfers as examples of alternative data while highlighting concerns about accuracy, consumer access to data, hard-to-explain factors and potential unlawful discrimination. Its legal discussion is specific to the United States, but the operational questions are relevant elsewhere.

Apparently neutral variables can reproduce disadvantage

A model may not use a protected characteristic directly yet still rely on variables that closely track social or economic exclusion. Sparse digital activity, for example, can reflect connectivity, geography, cash dependence or platform access rather than repayment behavior. Testing model performance across relevant borrower groups is therefore part of responsible underwriting, not an optional exercise after deployment.

The lender may know the platform better than the borrower

A marketplace or payments provider may have a rich view of sales conducted inside its system but little knowledge of the borrower’s wider finances. When credit is tied to one platform, the borrower may also find it difficult to transfer a useful history to another lender. This can turn a record of economic activity into a platform-specific asset rather than a portable financial reputation.

Prediction does not replace affordability assessment

A model can predict the probability of repayment without establishing that repayment will leave enough money for inventory, food, rent or emergencies. A borrower may repay by cutting essential spending, taking another loan or delaying supplier payments. Responsible lending therefore requires attention to repayment capacity, total cost, loan purpose and the timing of cash flows.

A checklist before sharing transaction data

A borrower or small-business adviser should ask the following questions before authorizing account access:

Exactly which accounts, platforms and transaction categories will be accessed?
What decision will the data inform, and will they be used for any additional purpose?
How much historical data will be collected, and for how long will the lender retain it?
Will the lender, a scoring company or another third party receive the records?
Can permission be withdrawn, and what happens to information already collected?
Can the applicant view the data, correct an error and request a human review?
Will the lender explain the principal reasons for a rejection or less favorable offer?
What are the total cost, repayment frequency, late-payment consequences and security requirements?
Does the repayment schedule match the business’s sales cycle and realistic net cash flow?
Is the lender regulated or otherwise subject to a credible complaints and recourse process?

Lenders and policymakers should ask a parallel set of questions: Is each input relevant to creditworthiness? Is the minimum necessary amount of data being collected? Does the model perform adequately for groups with sparse or irregular records? Can staff explain adverse decisions? Is there a workable correction and appeal process? The applicable legal duties vary by jurisdiction, so product design must be checked against local privacy, consumer-protection, credit-reporting and anti-discrimination rules.

The practical takeaway

Transaction data can turn an otherwise invisible payment trail into useful evidence of economic activity. Its strongest role is often to supplement thin conventional files, especially for digitally active microenterprises and workers. But a more predictive score is only one component of responsible credit access.

Borrowers should not have to exchange broad, indefinite access to sensitive records for an opaque or unaffordable loan. Lenders should collect relevant and proportionate data, assess net repayment capacity, explain important decisions and provide effective recourse. The next step for a prospective borrower is simple: assemble a representative transaction history, calculate realistic free cash flow, and ask how the lender will use—and protect—every record requested.