Frequently Asked Questions

Welcome to our FAQ page, designed to address common queries about Instnt's services. If you can't find the answer you're looking for here, please reach out to our support team with your question.

  • Instnt's fully managed acceptance platform empowers businesses to offer a seamless
    customer experience. It facilitates quick verification, acceptance, and onboarding
    of new online customers without incurring fraud losses or compliance issues.
  • Reason codes provide explanations behind Instnt Accept decisions of "Accept," 
    "Reject," or "Review" status for end-user signups. All information collected
    during signup is cross-verified against Personally Identifiable Information
    (PII) and other data from diverse sources. For additional details, please
    visit Reason Code Glossary.
  • Ensure that images are in JPEG format and not larger than 2MB for successful
  • Testing outcomes vary based on the environment. Our Sandbox environment only
    evaluates provisioned synthetic identities, automatically rejecting real
    identities. Ensure testing with real identities in the prod environment.
  • Insufficient information from our vendors can lead to unverified phone numbers 
    and addresses. Check Reason Codes NA102 and NP103 in the Reason Code Glossary.
  • Errors may relate to settings in the Trusted Domains during your workflow 
    creation. For detailed information, refer to Instnt CORS Implementation.
  • This might occur due to KYC watchlist hits or unverified personal information.
    Review provided reason codes for insights into specific information requiring
    verification. For further information, explore the Reason Code Glossary.
  • This innovative solution aids in immediate operational cost reduction and revenue
    growth. Leveraging integrated and automated AI technology, it enables
    the frictionless onboarding of more legitimate customers,
    minimizing fraud losses.
  • Financial institutions are mandated to implement Customer Identification Programs 
    (CIP), necessitating Anti-Money Laundering (AML) compliance and KYC verification
    for new customer onboarding. Instnt facilitates KYC checks within the Instnt
    Accept dashboard. KYC article provides further insights into its processing.
    There's no compulsion to use all features; businesses can opt for any
    combination as per their needs.
  • Instnt Accept™ serves as the primary line of defense, preventing fraud and 
    initiating a loss liability shift. It facilitates KYC checks, ensuring
    businesses can enhance digital services while offering identity
    assurance to accepted users, indemnifying businesses up to
    $100MM against fraud loss liability.
  • Instnt Verify™ offers continuous identity assurance, validating transactions from 
    authorized account owners. This solution effectively combats account takeovers,
    ensuring identity assurance from account opening to ongoing transactions.
  • Instnt Multipass complements Instnt Accept™ by leveraging Hyperledger blockchain 
    and web3 decentralized identity standards. It streamlines customer onboarding
    while minimizing risks from data breaches, offering decentralized identity
    capabilities and fraud loss indemnification of up to $100MM.
  • Utilizing these features entirely depends on your discretion and specific business
    needs. Refer to the KYC and field selector articles for a comprehensive
    understanding of these features.
  • No, the sequence of features in your onboarding form is entirely customizable as 
    per your team's preference.
  • No, integrating Instnt via SDKs or APIs provided by us will not alter your UI. 
    Only the data collected and submitted to Instnt is accessible to us,
    maintaining user anonymity during the signup process.
  • OTP is a feature verifying if the end-user possesses the provided phone number. It
    involves an SMS sent by Instnt to the applicant's phone number for validation.
  • Instnt has internally developed predictive fraud models for different fraud modes,
    categorized as 1st, 3rd party fraud models. These models, combined with other
    signals, assist in producing a final decision and are periodically updated
    to adapt to evolving fraud patterns.