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Informed Clear

caution

This feature is currently in active development and is subject to change. Please contact your account representative for more information.

Overview​

Capture rates may decline by as much as 50% when credit approval is contingent upon income verification. Increased friction within the origination process reduces the likelihood that dealers will present a given offer to the consumer. Furthermore, even when a conditional approval requiring income verification is presented, the process often becomes complicated; applicants may be required to leave the dealership to retrieve necessary documentation, a factor that significantly diminishes the probability of a successful funding.

Informed Clear is a non–FCRA-compliant, non-documentary validation product designed to help lenders rapidly evaluate fraud risk. Among its inputs are benchmarks for stated income versus similarly employed workers by occupation, employer and geography (Bureau of Labor Statistics Occupational Employment and Wage Statistics, BLS OEWS), along with dealer, vehicle and overall application characteristics.

Informed Clear provides lenders with comprehensive historical context on applicants. It surfaces prior encounters and details from past applications—reported information and verified information (identity, income, employment, residence and more) from official documents—so lenders can spot discrepancies or patterns that may indicate fraud.

Informed Clear also checks for credit stacking: whether the same applicant has appeared on another application in the past 30 days with signed dealer documentation already in place. Multiple vehicle purchases in a short window may not yet show on credit-bureau reports, so stacked deals can be hard to see through traditional credit data alone. This signal helps lenders understand whether the applicant may already be committed to another financed purchase and whether additional exposure is building across lenders and dealers before it is fully reflected on the bureau file.

tip

Together, these capabilities help lenders make faster, more informed decisions across origination and fraud operations—strengthening portfolio quality and the dealer experience without unnecessary process friction.

Process Flow​

The new Informed Clear service has the added benefit of not requiring a separate application registration step. You can now execute the Informed Clear call directly in combination with your initial registration request payload.

The POST call to the Informed Clear endpoint will return a synchronous response with the Informed Clear results and if your unique application_reference_id has not been previously registered, it will automatically register the application for you. The input payload and the output response are described in detail below.

You can make multiple Informed Clear POST calls for the same application as long as you are using the same application_reference_id. This is especially useful if you acquire updated information about the applicant or the vehicle after the initial registration. Just make sure to modify the request payload with the new information.

Later on, when you are ready to fund the loan, you can use all the existing functionality to update the application and upload documents as needed. Your unique application_id needed for the funding-time calls will be returned in the synchronous response to the initial Informed Clear call.

You will receive all the extractions and verifications callbacks to your designated webhook for the application at funding time.

Please reference the sequence diagram for the full process flow.

Request Components​

Authentication​

The Informed Clear call requires, and uses, the same authentication as the existing application registration call. Please refer to the authentication page for more details.

Base URL​

The table below shows the Base URL to be used with the endpoints mentioned in this guide.

EnvironmentBase URL
Staging:https://api.staging.informediq-infra.com
Production:https://api.informediq.com

Endpoint​

You will append the following endpoint to the base URL to make the Informed Clear call:

/v1/auto/informed-clear

Request​

So to put it all together, you will make a POST call to one of the following URL's based on the environment you are using:

https://api.staging.informediq-infra.com/v1/auto/informed-clear

or

https://api.informediq.com/v1/auto/informed-clear

Input Payload​

The combined Informed Clear and app registration call follows the same format as the existing application registration process. The example below and the following table highlight the key elements of the combined request payload.

Example Combined Registration/Clear Payload
{
  "application_reference_id": "unique-partner-application-id",
  "model_release": "v1.0.0",
  "application_date": "2026-05-21T09:11:38",
  "application_type": "Application",
  "applicants": {
    "applicant1": {
      "first_name": "Jane",
      "last_name": "Doe",
      "ssn": "123456789",
      "date_of_birth": "1990-05-15",
      "email": "jane.doe@gmail.com",
      "phone": "5551234567",
      "address_info": [
        {
          "address": {
            "street_address": "123 Main St",
            "city": "Los Angeles",
            "state": "CA",
            "zip": "90001"
          },
          "is_current": true,
          "monthly_housing_cost": 1850
        }
      ],
      "employment_info": [
        {
          "employment_type": "Employed",
          "employer_name": "school district",
          "occupation": "custodian",
          "income": {
            "amount": 5400,
            "period": "monthly"
          },
          "is_current": true,
          "start_date": "2024-03-15"
        }
      ]
    },
    "applicant2": {
      "first_name": "John",
      "last_name": "Doe",
      "ssn": "987654321",
      "date_of_birth": "1988-03-20",
      "email": "john.doe@yahoo.com",
      "phone": "5559876543",
      "address_info": [
        {
          "address": {
            "street_address": "123 Main St",
            "city": "Los Angeles",
            "state": "CA",
            "zip": "90001"
          },
          "is_current": true,
          "monthly_housing_cost": 1850
        }
      ],
      "employment_info": [
        {
          "employment_type": "Employed",
          "employer_name": "Fedex",
          "occupation": "driver",
          "income": {
            "amount": 4800,
            "period": "Monthly"
          },
          "is_current": true,
          "start_date": "2023-08-01"
        }
      ]
    }
  },
  "verifications": {},
  "dealer_info": {
    "dealer_name": "Baldwin Park Auto Mall",
    "dealer_reference_id": "DLR-123",
    "address": {
      "street_address": "480 N Baldwin Park Blvd",
      "city": "Baldwin Park",
      "state": "CA",
      "zip": "91706"
    }
  },
  "vehicle_info": {
    "vin": "1HGCV1F34PA000001",
    "make": "Toyota",
    "model": "Tacoma",
    "year": 2025,
    "condition": "Used",
    "odometer": 12000,
    "selling_price": 35000
  },
  "services": [
    "extract",
    "verify"
  ],
  "webhook": "https://your.callback.com/here"
}

The table below delineates a few of the important pieces to be aware of when implementing the combined application registration and Informed Clear call. These are important to consider for the overall optimization of your workflow with Informed.

Request ComponentNote
occupationThe occupation must be provided for each registered applicant in their employment_info object.
verificationsThe verifications object can be left empty for the initial Informed Clear call, but please see the note in the services row below if you do decide to plan ahead and include verifcations in your initial request.
servicesFor the initial Informed Clear call and registration, the services array is purely optional. However, if you register services up front, like extract and/or verify and include verifications, then the request must also follow all the normal guidelines presented in the register application page for the services and verifications that you are requesting. Please note that you can always make another call at a later time to update the application and add other services and verifications as desired.
vehicle_infoProviding the necessary vehicle info is required for the initial Informed Clear call in order to be able to provide the most accurate and precise response data.
dealer_infoProviding the necessary dealer info is required for the initial Informed Clear call in order to be able to provide the most accurate and precise response data.
webhookA webhook is not required for the initial Informed Clear call, as it is a synchronous response. However, if you are planning ahead to do other credit or funding verifications against the registered application, you will want to supply a webhook URL. Please note that you can always make another call at a later time to update the application and add a webhook URL value.
model_releaseThe model_release element is required for Informed Clear calls. It allows you to pin your request to a specific model version for scoring consistency. The model_release version should be set to v1.0.0 for now.
tip

If you don't have a co-applicant on the loan, it is important to completely remove the applicant2 object from the applicants object in the request payload.

Synchronous Response​

An example synchronous response payload is shown below:

{
  "application_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "clear_score": {
    "score": 1,
    "top_reasons": [
      "Stated income, housing expense and benchmark consistency"
      "Dealership identity and cohort-pattern signals"
      "Occupation identity, tenure and cohort-pattern signals"
    ]
  },
  "applicants": {
    "applicant1": {
      "auto_credit_stacking": {
        "flag": true,
        "app_count_30d": 2,
        "unique_lender_count_30d": 2,
        "unique_dealer_count_30d": 2
      },
      "bls_oews": {
        "mean_income": 62500,
        "median_income": 58000,
        "10pct_income": 35000,
        "25pct_income": 44000,
        "75pct_income": 76000,
        "90pct_income": 98000,
        "standard_occupational_classification": "37-2011",
        "data_source_version": 2024,
        "area_code": "31080",
        "human_readable_area": "Los Angeles-Long Beach-Anaheim, CA",
        "status": "MATCH_FOUND"
      },
      "historical_data": [
        {
          "application_date": "2025-06-10",
          "application_type": "Application",
          "verified_information": {
            "income_verified": true,
            "employer_verified": true
          },
          "reported_information": {
            "dealer_info": {
              "dealer_name": "AutoNation",
              "master_dealer_name": "AutoNation",
              "master_dealer_address": "100 Main St Los Angeles CA"
            },
            "vehicle_info": {
              "vin": "2T1BU4EE0DC000002",
              "make": "honda",
              "model": "civic",
              "year": 2024,
              "odometer": 8000,
              "selling_price": 28000
            },
            "employment_info": [
              {
                "employer_name": "walmart",
                "start_date": "2023-01-10",
                "occupation": "cashier",
                "income": {
                  "amount": 3200,
                  "period": "monthly"
                }
              }
            ],
            "address_info": [
              {
                "is_current": true,
                "monthly_housing_cost": 1200,
                "address": {
                  "street_address": "456 Oak Ave",
                  "city": "Los Angeles",
                  "state": "CA",
                  "zip": "90001"
                }
              }
            ]
          }
        }
      ]
    },
    "applicant2": {
      "auto_credit_stacking": {
        "flag": false,
        "app_count_30d": 0,
        "unique_lender_count_30d": 0,
        "unique_dealer_count_30d": 0
      },
      "bls_oews": {
        "mean_income": 52000,
        "median_income": 48000,
        "10pct_income": 30000,
        "25pct_income": 38000,
        "75pct_income": 62000,
        "90pct_income": 78000,
        "standard_occupational_classification": "53-3032",
        "data_source_version": 2024,
        "area_code": "31080",
        "human_readable_area": "Los Angeles-Long Beach-Anaheim, CA",
        "status": "MATCH_FOUND"
      },
      "historical_data": []
    }
  }
}

Response Components​

A successful API request will return a synchronous response with the Informed Clear results like the example shown above.

Application ID​

The unique Informed application_id will be returned in the response. You will need to use this value to update the application and add other services and verifications as desired in future API calls.

Informed Clear Score​

The clear_score object contains the Informed Clear score for the application. The score is a integer value between 1 and 99, where lower scores indicate lowest risk and higher scores indicate highest risk. The score is calculated based on the information provided in the request payload and the historical data for the applicant.

{
  ...
  "clear_score": {
    "score": 1,
    "top_reasons": [
      "Stated income, housing expense and benchmark consistency"
      "Dealership identity and cohort-pattern signals"
      "Occupation identity, tenure and cohort-pattern signals"
    ]
  },
  ...
}

The top_reasons array contains one or more text strings describing the top contributing factors behind the score, providing transparency into the model's assessment.

Applicant scoring​

Each applicant will have their own scoring sections within the applicants object comprised of three sections as described in the table below.

ElementsValuesDescription
auto_credit_stackingobjectThis section contains the auto credit stacking details for the applicant.
bls_oewsobjectThis section assesses how an applicant's stated income compares to that of similarly employed workers based on occupation, employer and geographic area according to the Bureau of Labor Statistics' Occupational Employment and Wage Statistics (BLS OEWS). More details can be found here.
historical_datalist of objectsThis section contains historical data regarding other credit applications by the applicant.

Auto Credit Stacking​

{
  ...
  "applicants": {
    "applicant1": {
      "auto_credit_stacking": {
        "flag": true,
        "app_count_30d": 2,
        "unique_lender_count_30d": 2,
        "unique_dealer_count_30d": 2
      },
      ...
    }
  }
ElementsValuesDescription
flagbooleanIndicates elevated concern for potential stacked auto financing (multiple financed purchases in a brief window).
app_count_30dnumericInteger count of applications or financed events attributed to this applicant in the past 30 days.
unique_lender_count_30dnumericInteger count of distinct lenders observed for this applicant in the past 30 days.
unique_dealer_count_30dnumericInteger count of distinct dealers observed for this applicant in the past 30 days.

BLS - OEWS​

For the U.S. Bureau of Labor Statistics (OEWS) data source, the key is bls_oews and the elements contained within are detailed below. More information regarding the BLS OEWS is available here.

{
  ...
  "applicants": {
    "applicant1": {
      "bls_oews": {
        "mean_income": 62500,
        "median_income": 58000,
        "10pct_income": 35000,
        "25pct_income": 44000,
        "75pct_income": 76000,
        "90pct_income": 98000,
        "standard_occupational_classification": "37-2011",
        "data_source_version": 2024,
        "area_code": "31080",
        "human_readable_area": "Los Angeles-Long Beach-Anaheim, CA",
        "status": "MATCH_FOUND"
      },
      ...
    }
  }
}
ElementValueDescription
mean_incomenumericMean yearly income based on the occupation and the area of the applicant as reported in BLS data

Or it can be null if there is insufficient data, an unsupported occupation, invalid input or no match found.
median_incomenumericMedian yearly income based on the occupation and the area of the applicant as reported in BLS data

Or it can be null if there is insufficient data, an unsupported occupation, invalid input or no match found.
10pct_incomenumericTenth percentile yearly income based on the occupation and the area of the applicant as reported in BLS data

Or it can be null if there is insufficient data, an unsupported occupation, invalid input or no match found.
25pct_incomenumericTwenty-fifth percentile yearly income based on the occupation and the area of the applicant as reported in BLS data

Or it can be null if there is insufficient data, an unsupported occupation, invalid input or no match found.
75pct_incomenumericSeventy-fifth percentile yearly income based on the occupation and the area of the applicant as reported in BLS data

Or it can be null if there is insufficient data, an unsupported occupation, invalid input or no match found.
90pct_incomenumericNinetieth percentile yearly income based on the occupation and the area of the applicant as reported in BLS data

Or it can be null if there is insufficient data, an unsupported occupation, invalid input or no match found.
standard_occupational_classificationstringStandardized Occupation of the applicant as per BLS definition. The list of occupations can be found here.

Or it can be null if there is an unsupported occupation, invalid input or no match found.
data_source_versionnumericInformed will use the latest version of the BLS data based on the date of the request, for example: 2022

Or it can be null if there is invalid input.
area_codestringThe area code is a string containing 2-7 numeric digits that represents a geographic area per the BLS data. It is returned as a string because there can be leading zeros. More information can be found below.

Or it can be null if there is invalid input.
human_readable_areastringA string containing a human readable description of the geographic area specified by the area_code above. More information can be found below.

Or it can be null if there is invalid input.
statusstringStatus of the BLS lookup. The five possible values are explained in the table below.

Historical data​

The historical data section contains a list of objects, where the objects represent past transactions associated with this applicant from our data consortium. Each object contains relevant data for that transaction showing both reported as well as verified information from that time.

Historical data
{
  ...
  "applicants": {
    "applicant1": {
      ...
      "historical_data": [
        {
          "application_date": "2025-06-10",
          "application_type": "Application",
          "verified_information": {
            "income_verified": true,
            "employer_verified": true
          },
          "reported_information": {
            "dealer_info": {
              "dealer_name": "AutoNation",
              "master_dealer_name": "AutoNation",
              "master_dealer_address": "100 Main St Los Angeles CA"
            },
            "vehicle_info": {
              "vin": "2T1BU4EE0DC000002",
              "make": "honda",
              "model": "civic",
              "year": 2024,
              "odometer": 8000,
              "selling_price": 28000
            },
            "employment_info": [
              {
                "employer_name": "walmart",
                "start_date": "2023-01-10",
                "occupation": "cashier",
                "income": {
                  "amount": 3200,
                  "period": "monthly"
                }
              }
            ],
            "address_info": [
              {
                "is_current": true,
                "monthly_housing_cost": 1200,
                "address": {
                  "street_address": "456 Oak Ave",
                  "city": "Los Angeles",
                  "state": "CA",
                  "zip": "90001"
                }
              }
            ]
          }
        }
      ]
    }
  },
  ...
}
ElementsValuesDescription
application_datestringA string representing the date of the original application using the following format:
YYYY-MM-DDThh:mm:ss
application_typestringA string representing the type of credit application, such as auto
verified_informationobjectThis section contains the status of any income and/or employer verifications that were completed at the time of the previous transaction.
reported_informationobjectThis section contains the information reported for the transaction, including details on the applicant, their address and employment information. If the transaction was an auto loan, it will also include relevant details on the vehicle and dealer involved.
tip

Please contact your account representative if you want historical data enabled for your paid use of the Informed Clear product.

Supporting information​

Status values​

There are five possible values for the status property of the response. The status value helps explain the outcome of the BLS lookup for a given applicant's information.

StatusDescription
MATCH_FOUNDA match was found in the BLS data and thus the income statistics will be supplied.
NO_MATCH_FOUNDOccupation/Area is incorrect or incomplete and doesn't have any match in BLS data. Therefore, the income statistics will be empty.
INSUFFICIENT_DATAThe BLS data doesn't have enough entries for meaningful statistics. Therefore, the income statistics will be empty.
UNSUPPORTED_OCCUPATIONThere are some occupations that will never have a match, regardless of area, and we want to make sure to distinguish these from the normal situation of no match for a given occupation/area. Examples include "Retired", "Pension", "Social Security", "Self-Employed". No income statistics will be supplied for this status.
INVALID_INPUT_FORMATThis status will result if the request payload was not properly formed. Examples might include leaving out a required field or submitting a numeric value where a string was expected. No income statistics will be supplied for this status.

Area codes and statistical areas​

Metropolitan Statistical Areas (MSA) are represented by a unique five-digit code per MSA. Example: 41940 (San Jose-Sunnyvale-Santa Clara, CA)

Non-metropolitan areas are represented by a seven-digit code made up of the state code followed by a numerical ID for the specified non-metropolitan area in that state. Example: 5300006 (Western Washington nonmetropolitan area -- in this case, the sixth such non-metropolitan area in Washington state, which has state code 53)

State codes (sometimes referred to as FIPS codes) are two-digit codes between 1-98 (left padded with a zero as necessary to maintain two digits for codes with a numerical value less than 10). Example: 06 (CA)

If none of the previous area codes are able to be determined, we will use the generic code for the USA, which is 99.

The detailed listing of all FIPS and MSA codes can be found here.

Lender Performance Data​

In order for the Informed Clear algorithm to constantly improve for your benefit, the algorithm needs feedback. That feedback will come from completing the loop on the loan applications that were scored at time of decisioning to record the ultimate status of the applications.

How this would look in practice is a monthly CSV file containing the information shown in the table below. A secure method of transfer will be established between Informed and the lender for the monthly file transfer from the lender.

info

Note that none of the information requested contains personally identifiable information (PII) about the loan applicants.

ElementsValuesDescription
application_idstringThe unique application ID designated by Informed at the time of application registration.
application_reference_idstringThe unique application ID assigned by the lender at the time of application registration.
application_statusstringThe current status of the loan application.
Acceptable values are approved, declined, funded
is_fraudbooleanIndicates whether the lender identified any fraudulent activity or material misrepresentation associated with the application.
Acceptable values are true, false
funded_datestringDate the loan was funded (if applicable).
Acceptable format: YYYYMMDD
loan_statusstringCurrent status of the loan.
Acceptable values are current, delinquent, paid_off, charged_off, repossessed
status_datestringStatus as-of date.
Acceptable format: YYYYMMDD
charged_off_amountnumericDecimal value of the loan charge off in dollars (if applicable).
charged_off_datestringDate of loan charge off (if applicable).
Acceptable format: YYYYMMDD
charged_off_reasonstringReason for loan charge off (if applicable).
Acceptable values are delinquency, fraud, bankruptcy, deceased, settlement, insurance_deficiency