fraud prevention

Fraud prevention software for banks and fintechs.

Payment fraud, account takeover, first-party fraud and scams on one decision engine, with one case queue and rules your team can change the same day.

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fraud prevention

Fraud prevention software for banks and fintechs.

Payment fraud, account takeover, first-party fraud and scams on one decision engine, with one case queue and rules your team can change the same day.

Get a Demo

See a Product Tour

fraud prevention

Fraud prevention software for banks and fintechs.

Payment fraud, account takeover, first-party fraud and scams on one decision engine, with one case queue and rules your team can change the same day.

Get a Demo

See a Product Tour

fraud prevention

Fraud prevention software for banks and fintechs.

Payment fraud, account takeover, first-party fraud and scams on one decision engine, with one case queue and rules your team can change the same day.

Get a Demo

See a Product Tour

Trusted by institutions building next-gen finance

use cases

Every fraud type, on one decision engine.

Most teams run a different tool for each of these, which is how the same customer becomes six unrelated alerts. One engine, one customer, one queue.

use cases

Every fraud type, on one decision engine.

Most teams run a different tool for each of these, which is how the same customer becomes six unrelated alerts. One engine, one customer, one queue.

Transaction fraud

ACH, wire, card, wallet and Zelle payments scored in real time, before they settle.

Transaction fraud

ACH, wire, card, wallet and Zelle payments scored in real time, before they settle.

First-party fraud

The customer is the fraudster. Behavioral and credit signals that separate genuine hardship from intent.

First-party fraud

The customer is the fraudster. Behavioral and credit signals that separate genuine hardship from intent.

Account takeover

Device, behavioral and session signals that catch a stolen session before money moves.

Account takeover

Device, behavioral and session signals that catch a stolen session before money moves.

Account opening fraud

Synthetic identities and bust-out rings caught at onboarding rather than three statements later.

Account opening fraud

Synthetic identities and bust-out rings caught at onboarding rather than three statements later.

Scams and APP fraud

Social engineering where the customer authorizes the payment themselves, which is why authentication alone never catches it.

Scams and APP fraud

Social engineering where the customer authorizes the payment themselves, which is why authentication alone never catches it.

Mule accounts

Accounts taking large or frequent deposits and moving them straight on, scored as a network rather than one at a time.

Mule accounts

Accounts taking large or frequent deposits and moving them straight on, scored as a network rather than one at a time.

the platform

One model, one case queue, one set of rules.

AML transaction monitoring

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the platform

One model, one case queue, one set of rules.

AML transaction monitoring

→

workflow

Change a rule in minutes, not a release cycle.

Describe the rule in plain language

Write what you want caught and Oscilar AI builds the workflow, rather than filing a ticket and waiting for a release.

Pre-built rules and ML models

Dozens of ready models and rulesets across payment fraud, first-party fraud and account takeover, or build your own.

Test before it ships

Back-tests, A/B tests and unit tests in one click, so a change is validated against your own history before it goes live.

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A risk workflow built from a prompt, branching through models and rulesets to an approve or deny decision

detection

Catch fraud on signals a rules engine cannot see.

A rules engine can only test what somebody thought to write down. Device, behavioral and network signals score the customer against their own history and against the accounts they are connected to.

Detect unusual transaction patterns in real time

Identify suspicious changes in user behavior

Device fingerprints and behavioral profiles

Cross-account linkage to uncover fraud rings

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efficiency gains

Fewer analysts per alert.

Intelligent prioritization

Cases ordered by urgency and severity and routed to the people who work that fraud type, so alerts get cleared 3x faster.

Holistic user view

Every historical and real-time signal about the customer in one place, so an investigation does not start with gathering.

AI case insights

A plain-language explanation of why the case exists, and what to look at next.

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An AI-written case summary recommending a decision on a flagged account, citing device and behavioural signals

Choosing between fraud prevention vendors?

Here is what to compare, and what to ignore.

How to choose fraud detection software

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"What stands out most to me is the strength of the partnership. The team's responsiveness, commitment, and consistency have truly earned my complete trust."

Rob M.

VP, Fraud Prevention

“The speed and commitment of their team stands out—they took us from contract to production in just 32 days when we urgently needed to replace our sunsetting platform.”

Yohay P.

Fraud Lead

“Oscilar’s platform feels modern and is a scalable solution that adapts to our evolving risks while maintaining easy usability. ”

Holden L.

Co-Founder

Customers see results with Oscilar.

Payments

How Nuvei Cut Manual Underwriting Time by 50%

Read Case Study

Payments

How Nuvei Cut Manual Underwriting Time by 50%

Read Case Study

Payments

How Nuvei Cut Manual Underwriting Time by 50%

Read Case Study

Financial Services

How SoFi Drives Performance With Risk Decisioning

Read Case Study

Financial Services

How SoFi Drives Performance With Risk Decisioning

Read Case Study

Financial Services

How SoFi Drives Performance With Risk Decisioning

Read Case Study

See Oscilar in action.

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