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.

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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.