[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-04-02。"],[[["This page details the data model, schema, and requirements for AML AI, which relies on understanding a bank's parties and their activities."],["The AML data model is divided into three main areas: core banking data (parties, accounts, transactions), risk investigation data (risk cases), and optional supplementary data."],["The data model includes tables such as Party, AccountPartyLink, Transaction, RiskCaseEvent, and PartySupplementaryData, each serving a specific purpose in risk detection and model training."],["AML AI performs data validation checks when a dataset is created, with details on errors and fixes available in the Data Validation Errors section."],["Data lineage is important, and it's recommended to take a snapshot of your BigQuery tables to preserve data integrity for AML AI operations."]]],[]]
