Mule accounts in banking – how fraud networks exploit accounts

Mule accounts explained

Mule accounts are accounts used to move illicit funds on behalf of criminals. The account holder might be complicit, coerced, or manipulated. Either way, mule activity fuels scams, account takeover, laundering, and cross-border fraud. If your monitoring is limited to one institution’s data, mule networks are easy to miss because the pattern is distributed.

What a mule account looks like in real life

Mule behavior is often less about a single transaction and more about a sequence: quick inbound funds, rapid outbound transfers, and a lack of normal spending patterns. Mule networks also reuse devices, identities, and counterparties across multiple accounts.

• Rapid pass-through of funds with minimal account balance retention
• Short account age paired with unusually high transfer volume
• Many inbound payments from unrelated parties followed by consolidation
• Frequent cash-out routes such as instant transfers, crypto off-ramps, or cash withdrawal

Why mule accounts are a priority for AML and fraud teams

Mule accounts sit at the overlap of fraud and money laundering. Stopping them reduces direct losses from scams and also disrupts laundering pipelines. Regulators expect institutions to identify suspicious patterns quickly and file the right reports when needed.

Common detection signals for mule networks

Detection works best when you combine transaction patterns with identity, device, and network signals. Single-rule approaches fail because criminals adapt fast and spread activity across channels.

• Velocity signals: sudden spikes in volume, frequency, or counterparties
• Network signals: shared beneficiaries, shared senders, or repeated routing paths
• Identity signals: reused credentials, suspicious onboarding behavior, inconsistent profiles
• Device signals: device sharing across accounts, location anomalies, unusual access patterns

Controls that actually reduce mule risk

Good controls are proportionate and measurable. They reduce risk without breaking legitimate customer journeys. Mature teams treat mule prevention as an end-to-end process from onboarding to monitoring to investigation.

• Risk-based onboarding and step-up verification for higher-risk profiles
• Dynamic transaction risk scoring and real-time monitoring
• Case workflows that link accounts, devices, and counterparties into one investigation
• Clear retention and review rules to avoid permanent labels and overreach

Why cross-institution visibility matters

Mule networks exploit silos. One bank sees a small piece of the flow and it looks normal. The full pattern only appears when multiple institutions can compare signals. The problem is that data sharing is limited by privacy rules, contractual barriers, and operational risk.

Your fraud controls are strong. The blind spots between banks aren't

Financial crime has evolved, but bank defenses have not. Sophisticated fraud rings now attack multiple institutions simultaneously, exploiting the "blind spots" between them.

 What's inside?

• 57 % of all fraud now begins at the onboarding stage

• The Problem: Blind spots & regulatory walls
  
  
• The Solution: A decentralised trust network

and more...

How privacy-preserving collaboration helps

Privacy-preserving computation makes it possible to share intelligence without sharing raw customer data. That means you can detect shared mule patterns and repeat counterparties across institutions while staying aligned with privacy and compliance expectations.

Where Partisia fits

Partisia enables privacy-safe collaboration using Multi-Party Computation so institutions can identify shared risk signals that point to mule activity, without exposing sensitive inputs. This supports better detection, fewer false positives, and stronger auditability.

Related reading: Suspicious activity monitoring, AML solutions, Federated learning in finance.