Other Scenarios
Others
Aggregate Difference (Transactions)
Generate an alert for a user or business where the difference between A deposits and outgoing B transactions is greater than X amount in Y period.
Best for:
- Regulatory limits
- Custom data
General Example:
For example, an entity might be suspicious it its total deposits – total outgoing transactions is over 300,000 NGN (the limit in Nigeria).
How It Works:
Select from the dropdown menu to complete the logic.
Alerted Transactions II
Generate an alert for entities with at least Y alerts with X amount and alert them again.
Best for:
- Previously alerted entities
- Alerted entities with extra conditions
General Example:
For example, an entity might be suspicious it its payroll changed drastically for certain employees.
How It Works:
Select from the dropdown menu to complete the logic.
Chainalysis Alert - Risk levels
Generate an alert if entity has X risk alert from chainalysis with Y amount.
Best for:
- Chainalysis
General Example:
For example, an entity might be suspicious if it is associated with one or more Chainalysis high risk alerts in a one-month period.
How It Works:
You must ensure that Chainalysis data is available in your transaction data. Then, select from the dropdown menu to complete the logic.
Multiple Occurrences
Generate an alert if a user or business triggers a rule X times (amount of triggers) in Y period.
Best for:
- Previous triggered rule required
General Example:
For example, an entity might be suspicious if it has flagged aa high velocity rule twice.
How It Works:
You must ensure there is a currently an active relevant rule. Then, select from the dropdown menu to complete the logic.
Insider Trading
Generate an alert if a user make a transaction similar to another user in X time.
Best for:
- Insider trading
General Example:
For example, an employee might be suspicious if he/she sells stock Y right after the CEO if the same company just sold stock Y as well.
How It Works:
You must ensure that the correct transactional data is available. Then, select from the dropdown menu to complete the logic.
Graph based rules
Generate an alert if a one or more users have similar information.
Best for:
- Risky links between entities
General Example:
For example, it might be suspicious if three different user account use the same credit card.
How It Works:
Learn how to use Graph based Rules.
Relative Transaction Amount Sequence
Generate an alert for entities with 2 consecutive transactions within a certain period.
Optionally, relative relationship between amounts can be set to achieve this condition - transaction_b
at amount X% of transaction_a
. Transactions can be further filtered by using embedded filters available for both transaction_a and transaction_b.
Best for:
- Sell low rule
General Example:
For example, an entity might be suspicious if it tries to sell stocks or crypto at a loss.
How It Works:
Fill in transaction_a
and transaction_b
correctly.
Simple Filters
Generate an alert if a user or business has X.
Best for:
- Most common rule
- Wide filter match
General Example:
For example, an entity might be suspicious if it tries to ACH funds to a previously flagged entity.
How It Works:
Select the "+ Add Rule" button to create your filter based scenario.
Simple Sequence
Generate an alert if a user or business exhibits the following X sequence of events in X time.
Legacy functionality
Please note, Simple Sequence is legacy functionality and is not fully supported on the platform today.
Best for:
- Requires action sequence to be coded as python
General Example:
Flag change in basic information over three times in 30 days: action.action_type in ('tax_id_numbers', 'addresses', 'phones', 'emails', 'password_change')
.
How It Works:
Select from the dropdown menu to complete the logic, then define a logic sequence using basic python and Unit21 fields.
Updated 9 months ago