Standalone Django service for rule-based anomaly detection on HOPE payment data.
📖 Documentation · Contributing
| Component | Description |
|---|---|
| Office | Represents UNICEF Country Office,equivalent of HOPE BusinesArea |
| Programme | Assistance Programme, same as in HOPE |
| PaymentPlan | Frozen/Serialized HOPE PaymentPlan |
| Component | Description |
|---|---|
| Rule | Logic used to analyse data |
| RuleConfig | Rule configuration, thresholds etc. |
| ProgramRuleConfiguration | Custom RuleConfig for specific Programme |
Anomalies can be performed against PaymentPlan (and included Beneficiaries), or at Household (only) level. Each rule is designed to receive PaymentPlan or Household informations.
AMS has two different flows, each pre (analyse) and post (detect) payment.
Analyse flow aims to detect anomalies BEFORE payment is executed to find data mismatch or anomalies in the beneficiary data
Detect flow uses payment information to search for anomalies (es. amount mismatch, excessive amount ....)