Most synthetic identity and first-party fraud applications look clean when they come in. The SSN checks out, the history is thin but plausible, and by the time the loss shows up, the "customer" is long gone. For banks, fintechs, and credit unions, spotting the risk is only half the problem. Acting on it, inside the approval process, is where manual work piles up.

Why? A fraud signal lives in one system; the decision happens in another. An analyst copies a score from a dashboard into a case, or worse, the score never reaches the decision at all and every suspicious application gets stuck in a slow review queue.

Oscilar's integration with SentiLink closes that gap. SentiLink’s  Synthetic Fraud Score flags falsified and manipulated identities, and its First-Party Fraud Score assesses whether an application shows patterns consistent with first-party fraud. Oscilar is the AI risk decisioning platform where those scores become decisions.

How the integration works

SentiLink's scores and reason codes flow into Oscilar as structured data, alongside the other signals a team already runs, so a fraud score stops being a number on a dashboard and becomes an input to the decision itself.

From there, your fraud team's existing decision rules take over. An application with a low-risk score and a clean rest-of-file can approve automatically. A high Synthetic Fraud Score can route the application to a step-up check, or to case management with the score, its reason codes, and the full application history together.

Because reason codes explain why a score landed where it did, reviewers start with context instead of a blank page. And since thresholds are adjustable, teams tune treatment to their own risk tolerance instead of accepting a one-size-fits-all cutoff.

What it looks like in practice

A fintech lender receives an application with a valid SSN and a two-year credit history. SentiLink's model, trained on hundreds of thousands of fraud labels assigned by SentiLink’s internal Fraud Intelligence Team, returns a high synthetic score with reason codes pointing at inconsistencies inside the identity. Instead of joining the general review queue, the application routes straight to a synthetic-fraud treatment. An eCBSV check confirms the SSN does not belong to the applicant, and the application is declined before a dollar goes out.

The same flow works in the other direction. Legitimately thin-file applicants - recent immigrants, students, young borrowers - get distinguished from synthetics instead of swept into the same bucket, so good customers do not pay the price for bad ones.

Each platform does what it does best

SentiLink tells you whether the identity is real, synthetic, or showing patterns consistent with first-party fraud, with the evidence behind the score. Oscilar decides what happens next, applying the team's own rules across every signal in the flow. The result is less manual review, faster decisions, and no gap between spotting a risky application and treating it like one.

Learn more: Bad Debt or Fraud? How to tell the difference in the data.