Sumsub Anomaly Detection is designed to help you monitor transactions by detecting anomalies with remarkable accuracy. It utilizes AI and advanced machine learning algorithms like Isolation Forest to identify activities or transactions that deviate significantly from established applicant behavior patterns, indicating potential fraud.

By continuously learning from transaction patterns, the system adapts to new types of anomalies, ensuring your financial operations remain secure.

This means you can trust it to timely spot irregularities and do the following:

Identify potential fraud activities as they happen, allowing immediate action.
Prevent significant financial losses by catching fraud early.
Enhance customer trust by ensuring their transactions are secure.
Provide tailored protection regardless of business size or industry.
Let your business meet regulatory requirements related to fraud prevention.

How it works

Sumsub Anomaly Detection identifies unusual financial activities that may require immediate investigation, and sends you real-time alerts or creates a case, rejects or puts the transaction on hold, adds tags, and so on.

Our AI also provides insights into why a particular transaction was flagged. It examines various factors and highlights the specific reasons for its decision, clearly understanding potential issues.

Each event or transaction gets an anomaly score: Low, Medium, or High, depending on the severity of the behaviour deviation, which is defined based on the signal types.

Use anomaly risk level in rules

You can use the anomaly risk level as a condition in your rules that would put on hold or block the transaction.

For example, you can select to auto-reject all transactions with a high anomaly score:

Create a rule as described in this article.
Set up the rule action as Reject.
Set up the rule condition as:
If
->
Field
->
anomalyExpressionData.anomalyRiskLevel
->
equals
->
Value
->
high
Proceed with adjusting the rule as needed and click Save when done.

Review results

The information about the anomaly score and the severity of the involved signals is available in the Anomaly Detector section of the target Transaction page.

The score is calculated based on the following signals.

Type of signal	Description
Aggregate	Signals based on calculating and comparing the applicant transactional patterns and history. This includes calculating a specific counterparty monthly/quarterly turnover, comparing applicants on the account to detect anomalies.
Applicant	Signals based on analyzing customer-provided details with the data from databases, documents, and third-party sources to detect anomalies.
Finance	Signals based on crosschecking the transaction data, such as the transaction method, institution, country, or amount to detect anomalies.
Counterparty	Signals based on analyzing counterparty data with the data from databases, documents, and third-party sources to detect anomalies.
Device fingerprinting	Signals based on the device data, such as IP addresses, device fingerprints, or statistics to detect anomalies.
Other	Signals based on all remaining risk factors not included in the other factors.