Complete the Lab
Lab: Predict Anomalies
Use Case: Anti Money Laundering
Anomaly detection models can be used in many domains to detect such things as network intruders, adverse health events, and network failures. On this mission, you will build an unsupervised anomaly detection model to detect money laundering using the DataRobot platform.
Mission format and duration: self-paced, hands-on, 45 minutes
Upon completion of this mission, you will be able to:
- Using a dataset of banking transactions, identify anomalies that indicate money laundering
- Build an unsupervised anomaly detection model in DataRobot
- Evaluate an anomaly detection model using Synthetic Area Under the ROC Curve (AUC) metrics generated by DataRobot
- Examine an anomaly detection model using Feature Impact, Feature Effects, ROC Curve, and the Anomaly Detection insight
- Run a test set to evaluate an anomaly detection model and compare the top blueprint with the top Synthetic AUC blueprint
Who should complete this mission?
- Business Analysts
- Citizen Data Scientists
- Data Scientists
Before embarking on this mission, you should complete one of the following:
- Starter Quest appropriate for your role (self-paced mission)
- AutoML I (virtual instructor-led mission)
- DataRobot for Data Scientists (virtual instructor-led mission)
- Chrome browser
- DataRobot Automated Machine Learning — If you don’t have access to the application, please sign up for our free trial: datarobot.com/trial.