SAMPLE Factori Person API USA B2B B2C ID Name Job level Postal Email Loan Insurance

Factori/SAMPLE Factori Person API USA B2B B2C ID Name Job level Postal Email Loan Insurance is a Credit & Lending data product published on databricks-marketplace and indexed by The Alternative Data Encyclopedia.

Factori publishes SAMPLE Factori Person API USA B2B B2C ID Name Job level Postal Email Loan Insurance as a databricks-marketplace offering in the Credit & Lending signal family.

The data

Factori maintains it as part of its Credit & Lending portfolio and lists it through databricks-marketplace.

Structure, access and licensing

License: other. Pricing: Free (Databricks Marketplace).

Access is through a dataset that is obtained from the databricks-marketplace listing. The listing does not state an explicit refresh schedule, which is worth confirming before backtesting.

The signal

Aggregated credit and lending data tracking consumer debt health: originations, balances, utilization and delinquency across mortgages, cards, autos and personal loans. The signal turns loan-book dynamics into an early read on the consumer credit cycle and lender fundamentals.

Delinquency inflections precede charge-offs, reserve builds and bank guidance changes by quarters, so lenders' equity reacts to cohort deterioration before management concedes it. Origination-mix shifts expose fintech growth or underwriting tightening ahead of disclosures, and aggregate consumer credit momentum informs rate-cut and consumption forecasts. Structured-finance desks use loan-level vintages to price securitizations against deal assumptions. Cohort-level series record originations by product and channel, outstanding balances, credit utilization and delinquency status, segmented by score band and geography. Vintage views follow each origination quarter's performance curve, while roll-rate matrices capture transitions between current, late and default states. Public benchmarks such as the New York Fed's household debt series provide quarterly aggregates built from anonymized credit reports.

Inputs combine credit-bureau partnerships covering anonymized credit-report panels, direct data-sharing agreements with banks and fintech lenders, and loan-level files from securitization trustees. Vendors anonymize records into cohorts with minimum cell sizes, then publish stock-and-flow series with vintage cuts that preserve point-in-time performance. Quarterly public series from central-bank research units serve both as calibration references and as products in their own right.

Caveats and compliance

Panel composition shifts silently and moves aggregates without any underlying borrower change. Regulatory reporting lags and restatements delay signals, and servicing transfers can masquerade as delinquencies. Balance definitions drift as products evolve, complicating long-history comparisons.

Among the most regulated categories alongside card data: GLBA and FCRA govern bureau-derived information, GDPR applies in Europe, and fair-lending rules constrain analysis split by protected attributes. Aggregation must prevent re-identification of small or unusual cohorts.

Who uses this signal

Bank, card and fintech equity analysts read origination and delinquency turns early; macro teams track household leverage as a consumption and policy input. ABS and credit investors benchmark pool performance against panel cohorts.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading