- User-configured rules (Flows)
- ProfiledRisk intelligence (behavioral and contextual scoring)
- allowed — event can proceed
- blocked — event should be stopped
- pending — event requires manual review before continuing
When To Use This Use Case
Use the Banking category when processing:- Deposit requests
- Withdrawal requests
- Wallet-to-wallet or account-to-account transfers
- Bill payments and other value transactions
- Security-sensitive account actions, including password or PIN updates
Expected Event Inputs
A Banking event should include signals that describe:
The full JSON schema and field definitions are documented here
ProfiledRisk will use all available fields to build risk context over time.
Decisioning Logic
Each Banking event is evaluated through:-
Your Flows
Rules configured in the dashboard using conditions such as amount thresholds, country restrictions, new devices, etc. -
System Intelligence
- User’s historical transaction behavior
- Device and IP history
- Geographic consistency
- Counter-party patterns
- Interaction velocity
Case Creation
A Case may be created when:- A rule instructs that additional verification is required
- System intelligence cannot determine clear fraud vs. legitimate behavior
- The returned status is pending
Example Flow Rules
Below are common rule patterns for Banking events: Scenario Flow Condition Returned Status Account takeover indicators New device + password changed recently + high withdrawal amount blocked Suspicious cash-out pattern 3+ unrelated incoming transfers followed by immediate withdrawals pending High-risk recipient First-time beneficiary in restricted region pending Abnormal activity from dormant profile No activity for extended period then large transfers blocked Rule sets can be expanded and tuned based on operational policy.Summary
ProfiledRisk helps protect banking operations by:- Preventing unauthorized account access and withdrawals
- Detecting mule activity and laundering flows
- Routing uncertain activity to human review before funds move
- Reducing operational workload through automated decisioning

