Anomaly Detection Scenarios
Anomaly Detection
Dormant Activity
Generate an alert if a user or institution suddenly revitalizes an account after a long period of dormancy.
Best for:
- Profile deviation
- Activity after a period of inactivity
General Example:
For example, an entity might be suspicious if it transacts $100,000 after being inactive for 60 days.
How It Works:
Complete the logic by selecting from the available dropdown items.

Historical Deviations A
Generate an alert if the transaction amount falls outside of the expected distribution (based on average transaction amounts).
Best for:
- Behavior deviation
- Transaction volume / amount significantly deviates from their past average transactions
General Example:
For example, an entity might be suspicious if it transacts 25% more in the last 30 days compared to its yearly average.
How It Works:
Complete the logic by selecting from the available dropdown items.

Historical Deviations B
Generate an alert if a user or business's transaction total amount differs by a specific amount.
Best for:
- Behavior deviation
- Delta from a specific period
General Example:
For example, an entity might be suspicious if it transacts $100,000 in the last 7 days whereas it transacted only $10,000 in the last 14 days.
How It Works:
Complete the logic by selecting from the available dropdown items.

Newly Seen
Generate an alert if a user or business's has a suspicious number of new actions or transactional information compared to the usual.
Best for:
- New behavior
- New actions and transactions never before seen
General Example:
For example, an entity might be suspicious if it typically transacts in 5 countries (France, Spain, Portugal, Greece and Italy) and suddenly transacts with 4 new (and never before seen) countries (Iran, Iraq, Lebanon and Jordan).
How It Works:
Complete the logic by selecting from the available dropdown items.

Updated almost 2 years ago