This bill directs federal regulators to analyze whether a set of nontraditional data points should be used in models that produce consumer credit scores, and how those models change creditor decision-making. It does not itself change scoring rules or compel use — it requires a cross‑agency study and a report to Congress.
The study's scope reaches payment histories (rent, utilities, telecom, insurance), account and transaction records (brokerage statements, depository account transactions, payroll deposits, peer‑to‑peer activity), public records on property and business licenses, and Buy‑Now‑Pay‑Later installment histories and EBT transaction records. For lenders, credit bureaus, data providers, and compliance officers, the report could shape future regulatory guidance and industry practice by supplying federal analysis of predictive value, fairness, privacy, and data quality issues.
At a Glance
What It Does
The bill requires the Director of the Consumer Financial Protection Bureau and the Chair of the Federal Trade Commission to jointly submit a report to Congress evaluating credit scoring models that incorporate certain additional 'key factors' and to assess how those models affect lender evaluations of consumer creditworthiness.
Who It Affects
Credit scoring firms, consumer reporting agencies, banks, fintech lenders (including BNPL providers), utilities and rent‑payment processors that collect payment data, payroll processors, and consumers with thin credit files or nontraditional financial histories.
Why It Matters
Federal analysis could legitimize or disfavor the use of alternative data in underwriting, influence enforcement or guidance from the CFPB and FTC, and surface unresolved questions about data accuracy, consumer protection, disparate impacts, and privacy controls.
More articles like this one.
A weekly email with all the latest developments on this topic.
What This Bill Actually Does
The bill is narrowly focused: it mandates a joint study and report. Congress asks two agencies—the CFPB and the FTC—to pool expertise on consumer financial protection and unfair or deceptive practices and tell lawmakers what happens when credit scoring models start to rely on nontraditional inputs.
The statute lists the types of information the agencies must consider but does not itself change any legal requirements for credit reporting or scoring.
Substantively, the bill channels attention to several classes of alternative data: payment streams (rent, utilities, telecom, insurance), account activity (brokerage statements, depository transaction records, payroll deposit frequency), certain government benefit transactions (electronic benefit transfer records), installment‑payment histories such as Buy‑Now‑Pay‑Later, public records tied to property and assets, and peer‑to‑peer payments. Bringing these disparate sources into scoring models raises technical questions—how to standardize and validate records, how to handle gaps and errors, and whether the signals are stable and predictive across populations.By tying the statutory definitions for ‘‘credit scoring model’’ and ‘‘key factor’’ to sections of the Fair Credit Reporting Act, the bill frames the study within the existing consumer‑reporting legal structure.
That link signals that the agencies should consider FCRA contours—accuracy obligations, dispute processes, and consumer notices—when evaluating alternative inputs. The text also leaves open the range of analytic approaches: the agencies must assess the mechanics and effects of using these factors, which invites examination of predictive validity, potential disparate impacts, privacy and consent issues, and operational burdens for data suppliers and users.Finally, the bill is a fact‑finding vehicle rather than a regulatory mandate.
It creates a federal spotlight: the agencies will produce findings that stakeholders will use to press for regulatory changes, industry adoption, or consumer safeguards. But the statute does not appropriate funding, set implementation timelines for private parties, or instruct agencies to issue rules; those would remain separate actions after the report circulates.
The Five Things You Need to Know
The bill directs a joint report to Congress from the CFPB Director and the FTC Chair.
The agencies must submit their report by December 31, 2025.
The statute enumerates 11 types of 'key factors' for the study, spanning brokerage statements, BNPL installment histories, EBT transaction records, rental and utility payment history, telecom and subscription payments, depository transaction data, payroll deposit frequency, insurance payment history, public records tied to property or licenses, and peer‑to‑peer activity.
The bill defines 'credit scoring model' and 'key factor' by reference to specific Fair Credit Reporting Act provisions (15 U.S.C. 1681g(f)(2)(A) and (B)), anchoring the study to existing consumer‑reporting law.
The text creates only a study and report obligation; it does not amend scoring rules, require industry action, or provide new enforcement authority.
Section-by-Section Breakdown
Every bill we cover gets an analysis of its key sections.
Joint report to Congress on alternative factors
This subsection requires the CFPB Director and the FTC Chair to jointly submit a report to Congress that covers two things: the use of credit scoring models that include the listed key factors, and how those models change creditor evaluations of consumer creditworthiness. Practically, the clause forces cross‑agency coordination between the consumer‑protection regulator (CFPB) and the unfair‑practices regulator (FTC), combining methodological assessment with legal and consumer‑protection lenses.
Enumerated key factors to be studied
Subsection (b) sets the study's scope by listing the data elements the agencies must examine. The list groups alternative data types—investment account records, installment‑payment and BNPL histories, EBT and payroll transaction records, rental/utility/telecom/subscription payments, insurance payments, depository transaction data, public records about property/assets, and peer‑to‑peer transactions. For each item the agencies will need to evaluate source reliability, standardization challenges, potential for error, availability across populations, and whether each item offers predictive power beyond traditional credit file data.
Definitions tied to the Fair Credit Reporting Act
The bill adopts the FCRA's definitions for 'credit scoring model' and 'key factor' by citation. That linkage narrows the study to models and inputs that fall within FCRA's conceptual framing—credit scores used in consumer reporting contexts—and signals that agencies should assess these factors against FCRA obligations such as accuracy, dispute handling, and permissible uses.
This bill is one of many.
Codify tracks hundreds of bills on Finance across all five countries.
Explore Finance in Codify Search →Who Benefits and Who Bears the Cost
Every bill creates winners and losers. Here's who stands to gain and who bears the cost.
Who Benefits
- Consumers with thin or no traditional credit files: Access to validated alternative data (rent, utilities, payroll) could improve scored access to credit for renters, newcomers, and others absent from traditional credit histories.
- Nonbank lenders and fintechs seeking better underwriting signals: Firms that already capture BNPL, payroll, or P2P data could gain regulatory analysis that supports incorporation of those signals into underwriting models.
- Data providers (rent‑payment processors, utility aggregators, payroll processors): The study spotlights the commercial value of their datasets and could increase demand for standardized feeds or certified data products.
Who Bears the Cost
- CFPB and FTC: The agencies must allocate staff and analytic resources to complete a cross‑cutting technical and legal study within the statutory timeframe.
- Credit bureaus and scoring companies: If the report prompts future rulemaking or private adoption of new inputs, these firms will face model‑validation, data‑ingestion, and compliance costs to integrate and vet new data sources.
- Small lenders and community banks: Operational and compliance burdens may rise if incorporation of alternative data becomes an industry expectation, requiring new vendor relationships and dispute‑resolution processes.
Key Issues
The Core Tension
The central dilemma is trade‑off between inclusion and risk: using alternative data promises to bring credit access to people lacking traditional files, but it simultaneously raises privacy, accuracy, and fairness risks—adopting more signals can improve prediction for some while amplifying errors or disparate impacts for others, and the bill asks agencies to analyze both sides without prescribing how to resolve tensions if the evidence points in conflicting directions.
The bill forces useful analysis but leaves large implementation questions unresolved. First, the statute mandates study of a disparate set of data types without prescribing analytic standards: agencies must decide what counts as adequate predictive validation, how to measure disparate impact, and which error‑rates are acceptable.
Those methodological choices will materially shape any regulatory or market follow‑on. Second, the listed data sources vary widely in provenance and governance—brokerage statements and payroll data come from regulated institutions, while peer‑to‑peer transactions and subscription payments are often held by third‑party apps with different accuracy controls—creating heterogeneity that complicates general conclusions.
The statute also threads the study through FCRA definitions, which raises practical tensions. Treating these inputs as within the FCRA sphere implies obligations around accuracy and dispute handling, but the bill does not allocate funding or set standards for how disputes would be handled for novel data sources.
Privacy and consumer consent loom large: incorporation of transactional or benefit‑related records into credit models raises distinct privacy and statutory concerns (for example, around government benefit use and sensitive data), yet the bill provides no framework for consent, minimization, or data use limits. Finally, the report requirement creates political and market signaling effects—findings can spur rapid industry adoption or regulatory action—but the statute itself stops short of setting thresholds, enforcement mechanisms, or timelines for any follow‑up action.
Try it yourself.
Ask a question in plain English, or pick a topic below. Results in seconds.