Companies & finance
Credit and fraud dataset
Support research on credit scoring, lending outcomes, payment fraud, and related labeled records. Field definitions and label notes explain the structure; data may be public, licensed, or reproducible synthetic data.
Request a sample ↗What to confirm before requesting a quote
- Coverage
- Regions, languages, date range and available record count
- Fields and attachments
- Field definitions, example records and whether original documents or media are required
- Quality and updates
- Deduplication and missing-value rules, sample-based acceptance criteria and update frequency
- Permitted uses
- Internal research, model training, customer-facing products or redistribution
These are project scoping questions, not a statement that every data type includes all coverage or permissions.
01 / WHAT’S INCLUDED
What’s in the dataset
- Credit and lending records
- Payment-fraud examples
- Field definitions
- Label notes
02 / USE CASES
Where it can help
- Model evaluation
- Risk-rule research
03 / DELIVERY
Choose the data you need
Tell us your topics, date range and required fields. Where the source permits, we’ll provide a sample first, then agree on filtering, cleanup and annotation.
Typical delivery formats are Excel, CSV, JSON and JSONL. The quote will state whether original documents or media are included. Let us know if you need recurring updates.
See a sample format ↗Assess the fit for your project
Credit and fraud data can support research around credit- and risk-related records from company or public and authorized sources. Intended use, source permissions, fields, and geographies must be defined first; projects do not supply sensitive personal information or make risk determinations.
How should a credit or fraud research use be bounded?
State whether the planned use is research, operational review, model development, or another purpose, and who will use results. Fields, sources, and permissions are assessed accordingly; data preparation or labeling is not a credit, denial, or other automated decision.
Does it include sensitive personal information?
It does not provide sensitive personal information. Scope requests around necessary company, public, or authorized records and explicit fields, avoiding personal profiling, private account details, or sensitive-attribute inference.
Sources and usage
Source type, permissions, labels, and intended use are agreed for each engagement.
If you plan to train a model, use the data in a customer-facing product or share it with third parties, mention this when requesting a quote so we can check source permissions and restrictions.
Public access does not establish permission for collection, training or redistribution. Naming a platform does not imply affiliation, agency or endorsement.
Read about data sources and rights ↗Want to review a sample first?
Tell us your project objectives and we’ll help you assess and find a relevant sample.