Financial Services
Banks and insurers decide about people they cannot see, and a face, a voice and a document can now all be produced by software. FaceOff confirms the person behind an account opening, a high value transfer or a claim, without adding a step the customer has to complete. Built to sit inside the RBI, SEBI, IRDAI and DPDP Act 2023 obligations your teams already report against
Overview
Check that the person opening an account, making a claim or approving a payment is really who they say they are, before the money moves.
Every decision a bank or an insurer makes about a new customer rests on one question: is this person who they say they are? An account is opened online, a payment is approved on a call, a claim arrives as a photograph and a recorded statement. Nobody in the room has met the person on the other side.
That question has become harder to answer honestly. A face on a video call, a voice on a phone line and a photograph attached to a claim can all be produced convincingly enough that looking harder does not settle it. Identity records can also be assembled from real details belonging to different people, so an application passes every check it is given while the customer never existed.
FaceOff answers the question at the moment it matters, which is before an account is opened, before money moves and before a claim is settled. Every result comes back with the findings behind it, written for the person making the decision rather than for a specialist, and in a form that survives a compliance review, a regulator and a court.
What you deploy
These are the products banks and insurers deploy most often, in the order they usually land: confirming a new customer, holding that confidence through the session, and checking the media that arrives with a claim or an instruction.
Autonomous voice interviewer with trimodal audio, visual and linguistic forensics.
ExploreKey capabilities
What this sector asks for most often. Each one runs at the moment a decision is being made, and each returns a result a reviewer can explain to somebody else.
Before an account is opened, the applicant is confirmed as a live person rather than a photograph, a recording or someone standing in for them. The same check confirms a customer approving a high value payment, so the approval is tied to the person rather than to a code that can be moved to another device.
Some applications are built from genuine details belonging to different people, which is why each detail checks out on its own. Records are read as a connected group instead, so a set of accounts that no unrelated customers would share becomes visible while those accounts can still be refused.
A session that was genuine at sign in can change hands part way through. Confidence in the customer is maintained quietly while the session is open, so a takeover during a transfer is caught while it is happening rather than found in a reconciliation weeks later.
No result arrives on its own. Each one carries the findings that led to it, so a reviewer can see what was noticed, act on it, and explain it later to an auditor, a regulator or a customer who has asked why.
Authorisation calls and recorded claim interviews are checked for speech that was generated or cloned rather than spoken. On a live call the result arrives while the conversation is still running, which is the only point at which it can change what happens next.
Images, video and recorded statements submitted with a claim are checked for signs of editing or AI generation. Where a file is long, the result points to the parts that deserve a closer look rather than leaving one verdict across the whole thing.
Where it is used
Every entry names a product on this site and describes it the way its own page does.
identities checked at account opening
the session checked while it is open
cloned voices caught on a call
fraud attempts scored before approval
connected identity records
Common questions
SyntheticFraudGuard AI reads identity records as a connected group rather than one application at a time. An identity assembled from details that belong to several real people passes every check applied to it alone, but the group of accounts it sits in does not look like a set of unrelated customers. Those applications are surfaced while the account can still be refused, and the connections behind the finding are shown so a reviewer can judge it instead of accepting a score on trust.
Yes. Speech on a live call is checked for signs that it was generated or cloned rather than spoken, and the result arrives while the call is still running, before an instruction is acted on. Recorded claim interviews are checked the same way afterwards, and BehaviourLens AI reads a recorded statement across voice, face and words together rather than listening to the words alone.
No. BehaviorBioAuth confirms the customer quietly while a session is open, so a genuine customer has nothing additional to complete and never sees a challenge that was not already there. Where a customer is asked to do something, such as approving a payment with Facepay, it replaces a step rather than adding one.
The checks are designed to confirm a person without leaving a bank or an insurer holding a new store of faces and voices, and analysis can run inside your own environment where material is sensitive or must stay in the country. Confirming a customer should reduce your overall exposure rather than trade one risk for another.
No. It sits alongside them and answers the question they were not built for, which is whether the person, the voice and the media in front of you are genuine rather than whether a transaction looks unusual. Findings are returned in a form your existing review, case and audit processes can carry without a new workflow.
Yes. It is built to sit inside the obligations banks and insurers already report against, including the RBI cyber framework, SEBI and IRDAI requirements, the Prevention of Money Laundering Act and the DPDP Act 2023. Every completed check leaves a record written to be read by somebody other than the team that ran it, which is what an audit or a regulatory review asks for.
Any method of detection can be mistaken in either direction, human judgement included, so a result is an evidence based assessment of risk rather than a guarantee. That is exactly why the findings come with it. The assessment is there to focus attention on what deserves scrutiny and to be examined and challenged, not accepted without question.
Bring your own footage. We will run it through the Adaptive Cognito Engine and walk you through the findings.
