# Disrupt Networked Mule Activity with Bureau’s Real-time Mule Score

Stay compliant with anti money laundering regulations with Bureau’s dynamic Mule Score. Assess risk by combining users’ digital footprints across multiple data points and deviations in behavioral changes to map transaction connections and expose mule rings even before the first transaction.

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Book a Free Demo](https://bureau.id/schedule-a-demo)

## Trusted by

### Stats
- 95%+ Mule catch rates pre-login
- 50% Decrease in false positives
- 1B+ Identities mapped for collusion and hidden fraud networks

# Tiered Mule Framework

A three-tiered approach that starts with onboarding scoring and progresses to cross-ecosystem mule detection with real-time interdiction alerts.

## Uncover the Complete Picture

Identify clusters of accounts transacting with each other, trace how funds flow within entities, and find key connector nodes that link multiple accounts.

### Higher Catch Rates, Lower False Positives

Improve mule catch rates with advanced AI models, ongoing performance monitoring, and feedback loops, without compromising user experience.

[Schedule a Demo](https://bureau.id/schedule-a-demo)

## The Bureau Difference

Identify and stop potential mule activity throughout the user lifecycle

### Frequently Asked Questions

#### What is money mule?
A money mule is an individual who unknowingly or willingly moves illicit funds on behalf of fraudsters to obscure its origin.

#### How do money mule networks operate?
Mule networks operate across multiple accounts and devices. They use stolen, fabricated, and synthetic identities to move illicit funds without getting detected.

#### How does Bureau detect and stop mule activity?
Bureau’s unified risk decisioning platform uses a three-tiered framework to detect and stop mule fraud rings. Using a dynamic Mule Score that is generated on the basis of real time behavioral signals, device intelligence, and digital footprint analysis, Bureau flags potential mule activity right from onboarding to every transaction.
