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A IA de AML tem como base um entendimento detalhado e atualizado
das partes do banco e da atividade delas, cobrindo, em particular, os
seguintes dados:
Atividade transacional
Ativos da conta
Informações demográficas da festa
Dados de investigação de risco
Esta página aborda a criação e o gerenciamento de dados usados pela IA antilavagem de dinheiro, incluindo detalhes do modelo de dados, do esquema de dados e dos requisitos de dados para a AML. O esquema em si, incluindo detalhes dos campos individuais, aparece no modelo de dados de entrada do AML (arquivo CSV). Um conjunto de dados sintéticos de exemplo também está disponível no Guia de início rápido.
Os pré-requisitos a seguir não são abordados nesta página:
O modelo de dados de AML aceita informações sobre partes comerciais ou de varejo, suas
contas e transações, além de informações detalhadas sobre casos de risco relacionados a
essas partes. Esta seção apresenta aspectos importantes do modelo de dados que
são válidos para as diferentes entidades.
O esquema do modelo de dados da AML é organizado em três áreas: dados bancários principais, dados de investigação de risco e dados suplementares.
Finalidade: serve como uma coleta estruturada de dados sobre seus clientes e
a atividade bancária deles, usada na detecção de risco. Todas as partes, contas e
transações a serem monitoradas precisam ser incluídas. Fornecer dados comerciais ou de varejo em um conjunto de dados de IA de AML
Finalidade: tabela opcional que pode conter informações adicionais relevantes
para identificar o risco de lavagem de dinheiro que não está coberto pelo restante do
esquema. Comece a usar a IA de AML sem fornecer dados
complementares.
Relações de tabelas
O diagrama a seguir descreve as relações de tabela, chaves primárias e
chaves externas.
Erros
Quando você cria um conjunto de dados, a IA de AML realiza automaticamente verificações de
validação de dados. Para informações sobre essas verificações, as mensagens de erro e
como corrigi-las, consulte
Erros de validação de dados.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-08-17 UTC."],[[["\u003cp\u003eThis page details the data model, schema, and requirements for AML AI, which relies on understanding a bank's parties and their activities.\u003c/p\u003e\n"],["\u003cp\u003eThe AML data model is divided into three main areas: core banking data (parties, accounts, transactions), risk investigation data (risk cases), and optional supplementary data.\u003c/p\u003e\n"],["\u003cp\u003eThe data model includes tables such as Party, AccountPartyLink, Transaction, RiskCaseEvent, and PartySupplementaryData, each serving a specific purpose in risk detection and model training.\u003c/p\u003e\n"],["\u003cp\u003eAML AI performs data validation checks when a dataset is created, with details on errors and fixes available in the Data Validation Errors section.\u003c/p\u003e\n"],["\u003cp\u003eData lineage is important, and it's recommended to take a snapshot of your BigQuery tables to preserve data integrity for AML AI operations.\u003c/p\u003e\n"]]],[],null,["# Understand the AML data model and requirements\n\nAt the core of AML AI is a detailed and up-to-date understanding\nof parties of the bank and their activity, covering, in particular, the\nfollowing data:\n\n- Transactional activity\n- Account holdings\n- Party demographics\n- Risk investigation data\n\nThis page covers the creation and management of data used by\nAML AI, including details of the data model, data schema, and\ndata requirements for AML. The schema itself, including details for the\nindividual fields, appears in the [AML input data model](/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model)\n([CSV file](/static/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model.csv)). A synthetic example dataset is also\navailable through the [Quickstart](/financial-services/anti-money-laundering/docs/train-models-to-detect-money-laundering).\n\nThe following prerequisites are not covered on this page:\n\n- Setup to use AML AI with an AML dataset (see [Set up a project and permissions](/financial-services/anti-money-laundering/docs/set-up-project-permissions))\n- Security and compliance features (see pages under [Security and compliance features](/financial-services/anti-money-laundering/docs/concepts/security-and-compliance-features))\n\nOverview of data requirements\n-----------------------------\n\nThe AML data model accepts information on retail or commercial parties, their\naccounts and transactions, and detailed information on risk cases related to\nthese parties. This section introduces important aspects of the data model that\nare valid across the different entities.\n\nThe AML data model schema is arranged into three areas: core banking data, risk\ninvestigation data, and supplementary data.\n\n### Core banking data\n\n- **Tables** : [Party](/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model#party), [AccountPartyLink](/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model#accountpartylink), [Transaction](/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model#transaction)\n- **Purpose**: Serves as a structured collection of data on your customers and their banking activity, used in detection of risk. All parties, accounts and transactions to be monitored should be included. Provide either retail or commercial data in an AML AI dataset\n\n### Risk investigation data\n\n- **Table** : [RiskCaseEvent](/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model#riskcaseevent)\n- **Purpose** :\n - Serves as a structured collection of data on risk investigation processes and parties previously identified as risky\n - Assists in the creation of training labels for AML risk models\n\n### Supplementary data\n\n- **Table** : [PartySupplementaryData](/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model#partysupplementarydata)\n- **Purpose**: Optional table that can contain additional information relevant to identifying money laundering risk that is not covered in the rest of the schema. You should start using AML AI without providing any supplementary data.\n\n### Table relationships\n\nThe following diagram describes the table relationships, primary keys, and\nforeign keys.\n| **Note:** Since AML AI uses data over time, primary keys may include `validity_start_time` to allow accurate representation of data over time. For example, AML AI can capture when a customer was added to or removed from a joint account. For more details, see [understanding how data changes over time](/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model#data-changes-over-time).\n\nErrors\n------\n\nWhen you create a dataset, AML AI automatically performs data\nvalidation checks. For information about these checks, the error messages and\nhow to fix them, see\n[Data validation errors](/financial-services/anti-money-laundering/docs/reference/data-validation-errors).\n\nFor more information about the technical schema, see\n[AML input data model](/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model)\n([CSV file](/static/financial-services/anti-money-laundering/docs/reference/schemas/aml-input-data-model.csv)).\nTo understand the data duration requirements and scope, see\n[Understand data scope and duration](/financial-services/anti-money-laundering/docs/understand-data-scope-duration).\nWhen you have tables ready in BigQuery, you can use\nAML AI to\n[create and manage a dataset](/financial-services/anti-money-laundering/docs/create-and-manage-datasets).\n| **Note:** Most model governance policies define a requirement to track data lineage used across all ML operations from engine configuration, training, and evaluation. To ensure data remains unchanged, we recommend that you create a snapshot of your BigQuery tables. In the case that you don't snapshot data, AML AI operations read the BigQuery tables each time an operation uses the dataset, so changes to the dataset could impact tuning, training, backtesting, and predictions."]]
