aml transaction monitoring

AML transaction monitoring software that clears its own alerts.

Score every ACH, wire, card, wallet and SWIFT payment as it moves, cut the false positives your analysts are buried in, and file the SAR from the same case.

aml transaction monitoring

AML transaction monitoring software that clears its own alerts.

Score every ACH, wire, card, wallet and SWIFT payment as it moves, cut the false positives your analysts are buried in, and file the SAR from the same case.

aml transaction monitoring

AML transaction monitoring software that clears its own alerts.

Score every ACH, wire, card, wallet and SWIFT payment as it moves, cut the false positives your analysts are buried in, and file the SAR from the same case.

aml transaction monitoring

AML transaction monitoring software that clears its own alerts.

Score every ACH, wire, card, wallet and SWIFT payment as it moves, cut the false positives your analysts are buried in, and file the SAR from the same case.

how it works

From transaction to SAR, in one system.

Four steps on one platform. Payments arrive from every rail you run, models score them in milliseconds, analysts investigate in a single queue, and the filing reaches FinCEN without a re-key.

how it works

From transaction to SAR, in one system.

Four steps on one platform. Payments arrive from every rail you run, models score them in milliseconds, analysts investigate in a single queue, and the filing reaches FinCEN without a re-key.

1. Ingest

Connect ACH, wire, card, wallet and SWIFT activity, and the customer data around it.

1. Ingest

Connect ACH, wire, card, wallet and SWIFT activity, and the customer data around it.

2. Score

Models score each transaction in milliseconds against customer behavior, not a fixed threshold.

2. Score

Models score each transaction in milliseconds against customer behavior, not a fixed threshold.

3. Investigate

Alerts land in one queue with the context already attached, so an analyst starts with the case rather than assembling it.

3. Investigate

Alerts land in one queue with the context already attached, so an analyst starts with the case rather than assembling it.

4. File

Pre-populated SAR templates and direct FinCEN e-filing, from inside the alert.

4. File

Pre-populated SAR templates and direct FinCEN e-filing, from inside the alert.

detection

Score every payment in real time, across every rail.

Batch monitoring tells you about last night. Real-time scoring lets you hold a payment before it settles, on the same models and the same rules.

Real-time scoring across ACH, wire, card, wallet and SWIFT payments

Pre-built typology rulesets you can edit without a release

Behavioral models with 95% detection accuracy that score the customer, not only the payment

Continuous learning as new typologies appear

An AML analytics view ranking alerts by priority, beside plug-and-play scenario templates and an alert prioritisation model
A risk workflow built from a prompt, branching through models and rulesets to an approve or deny decision
false positives

Tune thresholds against your own alert history, not a vendor default.

Every threshold change can be back-tested against your own data first, so you see what it would have caught and what it would have missed before it goes live.

Back-test any rule change against historical alerts

Segment thresholds by product, channel and customer risk

Backtest a threshold change in one click and see the alert volume it produces before you ship it

Keep a versioned record of every threshold change

investigation and SAR filing

Investigate and e-file without leaving the alert.

The case, the customer history, the related alerts and the filing all sit in one place, so an investigation does not open with fifteen minutes of gathering.

One queue for every alert, with context attached

Related-party and network view on the customer

Pre-populated SAR templates and direct FinCEN e-filing

An audit trail from alert through investigation to filing

Oscilar's command centre, showing total users, transactions and AML alerts above a compliance scorecard and suspicious-alert volumes

How compliance teams cut alert volume without missing activity.

FREE RESOURCE

How compliance teams cut alert volume without missing activity.

FREE RESOURCE

How compliance teams cut alert volume without missing activity.

FREE RESOURCE

How compliance teams cut alert volume without missing activity.

FREE RESOURCE

model governance

Show an examiner how the model decided.

Every score carries the reasons behind it, every rule and model has an owner and a version history, and every change is testable before it ships.

model governance

Show an examiner how the model decided.

Every score carries the reasons behind it, every rule and model has an owner and a version history, and every change is testable before it ships.

Explainable scores

Each alert names the features that drove it, in language an investigator and an examiner can both read.

Validation and tuning records

Every model version, every threshold change, who approved it and what the back-test showed, kept together so an examiner review is a lookup rather than a reconstruction.

Audit trail

An unbroken record from the transaction through the alert and the investigation to the filing.

advantages

Why compliance teams move off legacy monitoring.

Three things a legacy system makes hard, and this does not.

advantages

Why compliance teams move off legacy monitoring.

Three things a legacy system makes hard, and this does not.

Fewer false positives

Analysts clear alerts 3x faster and spend their time on the ones that turn into something.

Changes ship the same day

Update a rule or a threshold without a vendor ticket and without waiting for a release cycle.

One platform for fraud and AML

The same engine scores fraud and money laundering, so the two teams see one customer rather than two.

Eric Wright, Chief Operating Officer of CCBank

"Oscilar's expanded AML Risk platform capabilities, such as the GenAI Co-Pilot and Mission Control Dashboard, have the potential to be a game-changer for compliance operations. These innovations promise insights and oversight that were previously unimaginable."

Eric Wright

COO

CCBank logo
Eric Wright, Chief Operating Officer of CCBank

"Oscilar's expanded AML Risk platform capabilities, such as the GenAI Co-Pilot and Mission Control Dashboard, have the potential to be a game-changer for compliance operations. These innovations promise insights and oversight that were previously unimaginable."

Eric Wright

COO

CCBank logo
Michael Kozub, CEO of TransPecos Banks

"With Oscilar, we can update rules and implement changes faster than with any other platform we've seen."

Michael Kozub

CEO

TransPecos Banks logo
Michael Kozub, CEO of TransPecos Banks

"With Oscilar, we can update rules and implement changes faster than with any other platform we've seen."

Michael Kozub

CEO

TransPecos Banks logo
FAQ

Glad you asked.

What is AML transaction monitoring software?

AML transaction monitoring software reviews customer and account activity to find patterns that may indicate money laundering, then routes what it finds to an investigator. It covers the whole path: ingesting payments and the customer data around them, scoring activity against rules and models, raising an alert with enough context to work, and producing the suspicious activity report if the investigation supports one. Banks and fintechs run it because the Bank Secrecy Act requires a risk-based program, and examiners expect to see that the rules and models behind it are owned, documented and tested.

How is real-time monitoring different from batch monitoring?

Batch monitoring scores activity after the fact, usually overnight, so the first chance to act is the next morning. Real-time monitoring scores a payment as it moves, which means a transaction can be held, stepped up or released before it settles. For AML reporting the difference is smaller than it sounds, because a SAR is filed on a deadline rather than in the moment. Where it matters is the overlap with fraud: on instant rails there is no post-settlement recovery, so a decision made after the batch runs is a decision made too late.

How does AI reduce false positives without missing suspicious activity?

A fixed threshold treats every customer the same, so ordinary behavior for one account looks anomalous on another and the alert queue fills with activity nobody needed to see. Models score activity against what is normal for that customer and that segment, which narrows the queue without lowering the bar. The safeguard is testing rather than trust: a threshold change should be back-tested against historical alerts so you can see what it would have suppressed before it ships. Reducing false positives should come from better context, never from quietly suppressing alerts.

What is involved in replacing a legacy transaction monitoring system?

It does not have to be a replacement on day one. A bank can keep its upstream data sources and its incumbent system while running a new platform alongside it, comparing the two on the same activity until the coverage is proven. That parallel run is what gives model risk management something to review and what makes the eventual cutover defensible to an examiner. It also moves faster than most teams expect: Oscilar's AML Alert Triage agent typically deploys within 2 to 4 weeks, and Balance went from contract to full production in 32 days when it replaced a sunsetting platform.

How do you evidence a model decision to an examiner?

You need to be able to say who owns each rule and model, what version was running on a given date, what changed, who approved it, and how it was tested before it shipped. On an individual alert you need the reasons the score was what it was, in language a person can read. Keep the version history, the approval record, the back-test results and the ongoing performance monitoring together, and an examiner review becomes a matter of retrieving records rather than reconstructing them.

Does transaction monitoring cover fraud as well as money laundering?

They are usually separate systems and separate teams, which is why the same customer can be a fraud case in one queue and an AML case in another with neither team seeing both. Running them on one engine, sometimes called FRAML, gives you one view of the customer and one case standard, and the signals genuinely overlap: mule activity, account takeover and first-party fraud all leave patterns that matter to both. Oscilar scores fraud and AML on the same platform, so the two queues are views of one customer rather than two.