Detect organized crime networks, money mule schemes, and smurfing tactics that traditional AML rules might miss, all through digital footprints. Empower your compliance team to see not just individual transactions, but the relationships between users and accounts.
Follow the Network, Not Just the Money
Traditional rule-based systems can miss organized structures when they evaluate small, fragmented transactions that fall below financial thresholds in isolation.
In smurfing, money mule accounts, or interconnected user groups, risk often lies not in a single transaction, but in the relationships between accounts.
Truvali Network Detection analyzes the connections between users instead of evaluating them merely as isolated accounts.
By evaluating shared devices, IP relationships, transaction history, counterparty connections, and behavioral signals within the same framework, relationships between accounts that normally appear independent can be uncovered.
This allows compliance and fraud teams to answer not only:
"Is this transaction risky?"
but also:
"What other accounts is this user connected to?"
Visual Network Analysis for Compliance Teams
Tracking relationships between numerous accounts and transactions using only tables can be challenging.
Truvali supports the investigation of connections between users and accounts on a network structure.
Within the network, the following can be evaluated together:
- Accounts sharing the same IP
- Users connected to the same device
- Sender and receiver relationships
- Transfer networks
- Shared points connected to multiple accounts
- Suspicious account clusters
- Connections a few steps away
Thanks to this structure, analysts can examine the broader network of relationships surrounding a user, rather than focusing on a single user in isolation.
Help Reduce the Number of False Positives
A single risk signal does not always indicate strong suspicion.
For example, two users logging in from the same IP address might not be a significant risk indicator on its own. However, if these same users:
- Are transacting from the same device
- Are transferring to the same recipients
- Are active within similar timeframes
- Are part of the same transaction network
the risk level of the relationship changes.
Truvali helps build a broader context instead of generating alerts based on a single match by ensuring different risk signals are evaluated together.
The goal is not to generate the most alerts, but to make the relationships that truly need investigation more visible.
Risk and Volume-Oriented Actions
Network Detection is not limited to showing connections between accounts.
The transaction volume, user relationships, and other risk signals within the detected network can be evaluated alongside the institution's risk scenarios.
Depending on the risk level:
- The network can be silently monitored
- An alert can be generated
- A case can be opened
- The transaction can be routed to a manual review process
- Maker-Checker approval can be applied
- Actions can be transmitted to the institution's core system via callback
The action to be taken is determined by the institution's own risk policy and integration structure.
How Truvali Network Detection Works
- Digital Footprints Are Collected
Different signals associated with users and accounts are included in the evaluation.
These may include:
- Device ID
- IP information
- Phone number
- Transaction history
- Sender and receiver connections
- User behaviors
- Watchlist and blacklist results
- Relationships Between Accounts Are Established
Relationships are identified between users and accounts with shared signals.
For example, multiple accounts used on the same device or a shared recipient receiving funds from different accounts can create a connection point within the network.
- Network Risk Is Evaluated
In addition to the risk levels of individual accounts, relationships within the network are also evaluated.
Signals used in this evaluation may include:
- Connection density
- Shared device or IP usage
- Transaction volume
- Transaction frequency
- Connections to risky accounts
- Fund flow within the network
- Alert and Case Processes Can Be Initiated
An alert or case can be created based on the scenario defined by the institution for networks or relationships that exceed the designated risk level.
The compliance team can investigate related users and transaction history under the same case, rather than focusing on a single transaction.
Which Risks Does It Help Detect?
Truvali Network Detection can support the investigation of the following scenarios:
- Money mule networks
- Smurfing and structuring scenarios
- Multiple accounts used on the same device
- Suspicious account groups sharing the same IP
- Organized fraud networks
- Interconnected transfer chains
- Account clusters using a shared recipient or sender
- Indirect connections to risky accounts
The goal here is not to assume that a connection is proof of crime on its own, but to enable analysts to more easily investigate relationships that are normally difficult to detect.
See the Big Picture
Financial crime and fraud scenarios do not always play out through a single account or a single transaction.
Small activities that appear independent can turn out to be parts of a larger structure when analyzed at the network level.