Credit score proxy
Groups individuals into 12 ranks across 5 tiers (Best, Above average, Average, Below average, Poor); each tier spans 2-3 ranks. Rank 1 (~760 credit score) is best, rank 12 (~590) is lowest. Lower rank = higher score. The attribute comes from an estimate of how likely U.S. household are to pay on a marketing offer. It leverages age, property and residential data, lifestyle and purchase behavior, automotive data, an income estimate, and aggregate credit data. The attribute proxies credit score and does not use credit bureau data. Known or imputed for 257.6 million individuals — 100.0% of Faraday's identity graph.
At a glance
- Coverage today
- 100.0%
- Typical coverage
- 98.1%
- Individuals with a value
- 257.6M
- Median
- 4
- Most values come
- from the person
- Latest collection
- Jun 1, 2026
Distribution
How values are spread across the records that have one. Use it to check that the attribute tells records apart: when one value holds nearly everyone, it will not separate audiences much. The bars run in rank order, from the lowest value to the highest. The shading groups the bars into percentile bands.
Share by value
Share of individuals with a known value in each band. The middle 50% falls between 2 and 8, shaded darkest.
P10 means 10% of individuals have a value at or below this one, and P90 means 90% do. P25 and P75 bound the middle half. The median is the middle: half are below it, half above.
Median over time
4 → 4 across 12 collections, shaded between P25 and P75. Estimated from a 1% sample of individuals.
What the codes mean
12 codes
- 1
- Best - ~760 credit score
- 2
- Best - ~740 credit score
- 3
- Above average - ~725 credit score
- 4
- Above average - ~710 credit score
- 5
- Average - ~690 credit score
- 6
- Average - ~675 credit score
- 7
- Below average - ~660 credit score
- 8
- Below average - ~650 credit score
- 9
- Below average - ~640 credit score
- 10
- Poor - ~625 credit score
- 11
- Poor - ~610 credit score
- 12
- Poor - ~590 credit score
Coverage
How widely Faraday has this attribute, and how precise its values are. How many of your own records get a value also depends on how many of them Faraday matches. The state map shows where coverage is thin. Coverage over time shows whether it is growing or shrinking.
Coverage by state
Percentage of individuals in each state with a value.
Coverage over time
Share of individuals holding a value at each monthly snapshot. A value carries forward for one year.
How we arrive at a value
Whether a value was observed on the person themselves, carried from their address, or estimated from the people around them.
- On the person — 100%
- A named person, from a record about that person.
- From address — 0%
- Not used for this attribute — no value is inherited this way.
- From geography — 0%
- Not used for this attribute — no value is inherited this way.
Coverage, in context
Where 100.0% sits on the catalog’s own coverage range.
Higher coverage than 47% of individual-level attributes in the catalog
Freshness
Whether the values reflect today. A long gap since the latest collection means they may lag behind what is true now.
Collections
How recently this was collected, and how often a new collection arrives. Each bar is one collection, sized by how many individuals it carried. Gaps are real — collection is periodic, not continuous.
Change between collections
How much values move between collections. Income or life events should move; heavy change in something steady can mean noisy data.
of individuals changed value between the two most recent collections — 2.3× the catalog median of 16.0%.
Contribution ledger
14 ingestions1 authority contributed since Jul 2023.
| Observed | Ingested | Count | ID |
|---|---|---|---|
| 263,433,649 | 74d6c94c-8e2b-48fe-a704-50c99ad3cdde | ||
| 266,506,942 | c5492806-a892-460e-aba1-ee78ffb29c5b | ||
| 264,096,977 | 1181277b-b751-4a59-aae1-101fa3f55375 | ||
| 264,777,400 | af53c569-4fe2-4282-975b-4cbf5f85692d | ||
| 268,508,091 | 84d6a1c5-d142-4a4d-9cb3-902de4c82c30 | ||
Related attributes
Attributes whose descriptions are closest to this one.
Use it in the API
Send an email, phone, or address. Get Credit score proxy back in real time.
- Add
credit_score_proxyto a project. - Send an email, phone, or address to the project's append endpoint. The response carries Credit score proxy for the matched person.
curl -X POST 'https://api.pro.faraday.ai/v1/projects/YOUR_PROJECT_ID/append' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
"email": "jane.doe@example.com"
}'→ {"attributes":{"credit_score_proxy":"Above average - ~710 credit score"}}Technical details
null in any row means the label does not apply, or is not recorded, for this attribute.
- API name
- The field to request through the API, and the key it comes back under in a payload.
- credit_
score_ proxy - Category
- The catalog group the attribute is filed under.
- fig/financial
- Type · unit
- The kind of value and, where it has one, its unit of measure. Counts on this page are in individuals, whatever the unit.
- Integer
- Statistical type
- How the values are meant to be read: a measured amount, one of a ranked set, one of an unordered set, or several from an unordered set. The chart's form comes from the published values; for categories without a rank prefix, this label decides whether they are listed in rank order or by size.
- ordinal
- Allowed values
- The full list of values, when the attribute has a fixed set.
- null
- Derivations
- How the value is produced, each with its definition.
- Modeled
- — the attribute is calculated using an algorithm that takes known records as input.
- How values combine
- How the values from the attribute's sources become the one value delivered.
- Uses the first available of up to 2 source fields, in priority order.
- Directionality
- For ordered values, what a higher value means.
- Lower rank values indicate a higher modeled credit score and greater marketing profitability; rank 1 is best, rank 12 is poor.
- Deprecation
- True when the attribute is being retired; choose another for new work.
- false