Anomaly Detection for Assistance Delivery

Humanitarian operations generate massive volumes of registration and transaction data, often captured under pressure and in rapidly changing environments. Inconsistent formats, manual data entry and limited visibility make it difficult to spot irregularities, detect fraud or identify operational errors early. As a result, resources risk being misallocated and programme teams spend significant time on manual reviews instead of serving communities.

ADAD uses advanced machine learning to automatically detect unusual patterns across registration data and cash transactions. These models uncover both known and previously unseen anomalies, ranging from inflated household sizes to recycled identities, while prioritizing privacy, explainability and human oversight. This leads to faster, more accurate and more accountable decision-making across WFP.

ADAD uses a state-of-the-art contractive architecture, developed by CERN. Just as CERN uses advanced algorithms to sift through data at the Large Hadron Collider to find rare, new particles, ADAD is now using that same power to protect humanitarian aid. Instead of physics data, the AI learns typical household delivery patterns. This allows WFP to spot unforeseen irregularities and suspicious outliers without knowing exactly what to look for beforehand.

The ADAD project team presented a minimum viable product and validated the model performance with test data from the Somalia country office. ADAD's dashboard provides a comprehensive overview of the registration data of a WFP country office, as well as a deep-dive into the anomalies and financial exposure. The tool flags anomalies for review but never makes automated decisions regarding beneficiaries.

In a next step, the team will conduct an extended pilot in Somalia to gather further insights on impact before the project expands to transaction data.