compliance_monitoring · workflow
DoorDash builds anomaly detection platform to surface fraud trends in under three days
DoorDash's fraud team was reactive, only discovering new fraud trends after they had grown unchecked for weeks and begun impacting top-line metrics; the average time-to-detect exceeded 100 days.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Airflow daily job trigger
The anomaly detection platform runs as a daily job coordinated by Airflow to look for fraud trends growing on a day-to-week timescale.
Tools used
AirflowDuckDBSparkPython
Outcome
The platform now surfaces more than 60% of all new fraud trends, reduced average time-to-detect from more than 100 days to less than three days, and saves tens of millions of dollars per year.
Results
Time savedless than three days
Volume100%
Cost replacedMore than 60%
Grounding & classification
Source type: technical build writeup
25 fields verified against source quotes.
anomaly detectionfraud detectionhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedlogisticsautomation ratecost reductioncycle time reductiontechnical build writeupcompliance monitoringfinance opsextract classify routemonitor detect alert