Understand the AML data model and requirements

At the core of AML AI is a detailed and up-to-date understanding of individual customers of the bank covering, in particular, the following:

  • Demographics
  • Account holdings
  • Transactional activity
  • Transaction graph
  • Risk investigation activity

This page covers the creation and management of data used by AML AI, including details of the data model, data schema, and data requirements for AML. The schema itself, including details for the individual fields, appears in the AML input data model (CSV file).

The following prerequisites are not covered on this page:

Overview of data requirements

The AML data model combines information on retail and commercial parties, their accounts and transactions, and detailed information on risk cases related to these parties. This section introduces important aspects of the data model that are valid across the different entities.

The AML data model schema is arranged into three areas: core banking data, risk investigation data, and supplementary data.

Core banking data

  • Tables: Party, AccountPartyLink, Transaction
  • Purpose: Serves as a structured collection of data on your customers and their banking activity, used in detection of risky characteristics and behaviors

Risk investigation data

  • Table: RiskCaseEvent
  • Purpose:
    • Serves as a structured collection of data on risk investigation processes and parties previously identified as risky
    • Assists in the creation of training labels for AML risk models

Supplementary data

  • Table: PartySupplementaryData
  • Purpose: Contains additional information relevant to identifying money laundering risk that is not covered in the rest of the schema

AML data model schema diagram

For more information, see AML input data model (CSV file). When you have tables ready in BigQuery, you use the AML AI to create and manage a dataset.


When you create a dataset, you might encounter one or more data validation errors. For information on how to fix these errors, see Data validation errors.