ID Score is a probabilistic score (0-1) assigned to all users at the time of onboarding that predicts the likelihood that the identity supplied belongs to the person who is creating the account. 1 meaning we are 100% certain that the user is who they say they are. 0% means with 100% certainty we can say that the user is not who they say they are.
When ID Score is enabled, for users who have a high enough ID Score, we will only collect Base Document and SSN to onboard a consumer (individual or joint account). While for users with low ID Score, we will connect Government Issued ID and Video Auth as well.
At the beginning of the user onboarding process, nearly every end-user submits the following data points:
Date of Birth (DOB)
We then evaluate this submitted data. For example, we will place additional scrutiny (including restrictions) on IP addresses associated with:
Known web crawlers
Anonymity networks (e.g. Tor)
Sometimes our model will need to account for user-submitted information that may not necessarily be an indicator of fraud. To delve deeper into a location example, a user may submit a phone number with a 415 area code (i.e. associated with the San Francisco Bay Area) while listing a current address in Los Angeles--and our model may be able to disregard the location mismatch if we can find evidence of previous San Francisco residency in their address history and we can independently verify that the user presently resides in Los Angeles. We refer to such 2nd-order logic as “derived features” of ID Score.
Three main factors are considered for determining which vendors are most useful for ID Score:
Decisioning Tools: Ensuring that we are aligned on how the vendor determines what data to pass on to use and how they make assessments about trustworthiness.
Coverage: Ensuring that there are no gaps or redundancies in the data.
Sources: Ensuring that certain groups are not underrepresented or overrepresented.
After receiving the augmented data from other identity verification vendors, we use machine learning models to compare against previously-seen data patterns.
When you choose to integrate ID Score, we will ask you to pick one of the three preferences:
Your tolerance for Identity theft is very low, as a result you are a willing to collect additional documentation from a high volume of your valid users to reduce the likelihood of identity theft.
Your tolerance for Identity theft is moderate and you are a willing to collect additional documentation from some valid users to reduce the likelihood of identity theft.
You want to reduce onboarding friction for your customers and are willing tolerate some heightened exposure to fraud as a result.
Based on your Risk Appetite, we will recommend an ID Score that will serve as a threshold.
Users whose scores’ lie above this threshold will be part of a “Non-EDD” flow wherein the base document and SSN will suffice for onboarding of an individual. Users below this threshold score will be subjected to a “EDD” flow to further validate the users’ profile using additional verification tools.
Step 2: GET User to check ID Score .
Step 3: If ID Score is below the ID Score Threshold, upload EDD Docs.
 ID Score is located under the documents object of the user document (
id_score). If you are opening accounts for business or joint account users, please note that each base document has an ID Score value associated with it.
After ID Score has been added in your MSA (Master Services Agreement), we will follow the following steps:
We will analyze your platform’s activity and make ID Score Recommendations for all three Risk Appetites.
Based on which ID Score Threshold you pick, we will program that for you in your sandbox environment.
You will then, Integrate with our API for ID Score.
Test both EDD and non-EDD flows for ID Score in Sandbox.
Once testing completes, we will enable the threshold for you on production.