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AML AI is currently available to select Google Cloud customers.
To find out more or sign up to use AML AI, contact your
Google Cloud sales representative.
Step
Description
1. Prepare your Google Cloud project
Ensure your Google Cloud project is ready to use AML AI.
Review the project and the
security architecture
with your security and compliance teams.
2. Set up AML AI
Enable BigQuery, Cloud KMS, and the
AML AI API. Set up a customer-managed encryption key (CMEK)
to encrypt any data created by AML AI. Create one or more
AML AI instances. Set up logging and quotas.
3. Prepare data for AML AI
Review the data model and schema. Prioritize which data to include.
Collect and transform the necessary core banking data, risk investigation
data, and any other data you need. Validate and create a dataset.
4. Generate a model and evaluate performance
Configure an engine. Let AML AI train and evaluate a
model using your dataset.
5. Generate risk scores and explainability
Register your retail and commercial banking customer. Use a model to
generate per-party risk scores and explainability for use in these
subsequent steps:
Additional analysis and review of examples for risk governance
Testing and ramp up to production use
6. Prepare for model and risk governance
Combine AML AI outputs from tuning, training,
evaluation, and prediction with AML concept and product documentation to
meet requirements of your model risk governance process.