Analysis Result - Onboarding

The onboarding result is described as follows:

{
    "applicant_id": "ap-...",
    "reasons": [],
    "customer_data": {},
    "document": {}
}

	Parameter	Type / default	Note
1	applicant_id	string	The same applicant_id you receive when getting a token
2	reasons	list[string]	If the onboarding is accepted, list empty. If rejected, this will indicate the reasons of the rejection
3	customer_data	object / {}	When using the callback, ShareID can return the customer_data you provided. This information is for your internal use and will only be included in the callback
4	document	object	The document field contains the information extracted from the user ID document and the analysis result see below for details

1.applicant_id

The applicant_id uniquely identifies the user in our system. You should keep track of this value in order to fetch the result later. If you're using a callback, the applicant_id will also be send to your server (check here result on the callback).

"ap-...

2.reasons

The list of rejection reasons among the values below. This list is empty if the onboarding is accepted.

doc_expired	document expired
no_doc	document blurry, occluded or poorly illuminated
doc_spoof	document printed or shown on screen (spoofing)
doc_inject	document injected or digitally modified
doc_mismatch	document front and back do not match
doc_fraud	fraud suspicion on document
no_face	face detection problem: blurry, occluded or poorly illuminated
face_spoof	face printed or shown on screen (spoofing)
face_inject	face injected or DeepFake
face_fraud	fraud on document ownership
multiple_faces	Multiple faces present in the video
analysis_failed	Analysis has failed
operator_failed	Operator has failed
name_not_extracted or surname_not_extracted	Name or surname couldn't be extracted

3.customer_data

Any data you sent with the onboarding request here: getting a token

4.document

This field contains the information extracted from the user ID document and the analysis result.

{
    "type": {
        "doc_type": "",
        "doc_model": "",
        "country_code": "",
    },
    "ocr": {
      // Personal data extracted from user document -- see examples below  
    },
    "doc_front": "",
    "doc_back": "",
    "face_face": "",
    "face_doc": ""
}

Field	Type
doc_type	string	one of: "id_card" "passport" "driver_permit" "residency_permit"
doc_model	string	internal model reference detected
country_code	string	country code ISO 3166-1.alpha-2
doc_front	string | null	base64 encoded image of document front page
doc_back	string | null	base64 encoded image of document back page
face_face	string | null	base64 encoded image of face from liveness
face_doc	string | null	base64 encoded image of face from document

As you'll receive the images in a json document they will be encoded in base64. So you will need to decode them on your side.

Note that those images are not sent by default because it makes the json document largely heavier.

Result examples

{
    "type": {
        "doc_type": "passport",
        "doc_model": "2006",
        "country_code": "fr",
    },
    "ocr": {
      "doc_num": "", //if it appears on the document
        "surname": "",//if it appears on the document
        "alternate_name": "",//if it appears on the document
        "widow_of": "",//if it appears on the document
        "mariage_name": "",//if it appears on the document
        "name": "",//if it appears on the document
        "height": "",//if it appears on the document
        "birth_place": "",//if it appears on the document
        "birth_date": "",//if it appears on the document
        "address": "",//if it appears on the document
        "expiration_date": "",//if it appears on the document
        "issuance_date": "",//if it appears on the document
        "issuance_place": "",//if it appears on the document
    },
    "doc_front": "",
    "doc_back": "",
    "face_face": "",
    "face_doc": ""
}