[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85690-en":3,"doc-seo-85690-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85690,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Secure ERP Data Provisioning for Financial Control Testing","Financial control testing increasingly relies on representative enterprise resource planning (ERP) data in quality environments, but copying production data risks exposing personal, supplier, banking, and other commercially sensitive records. Secure ERP Quality Provisioning for Financial Control Testing (SEQ-FCT) provides governed data provisioning using deterministic masking, synthetic scenario expansion, referential tokenization, policy-based release approval, and automated validation. Evaluation uses one synthetic dataset of 186,000 finance-process records across six subsidiaries for 2022–2025, covering AP, AR, general ledger, and bank statements.","Secure ERP Data Provisioning for Financial Control  \nTesting  \nAnitha Samudrala  \nAMAZON CORPORATION  \nAbstract-Financial control testing increasingly depends on representative enterprise resource planning (ERP) data in quality environments, yet direct production copies expose personal, supplier, banking, and commercially sensitive records. This work presents Secure ERP Quality Provisioning for Financial Control Testing (SEQ-FCT), a governed dataprovisioning framework that combines deterministic masking, synthetic scenario expansion, referential tokenization, policybased release approval, and automated validation for reconciliation, fraud-rule testing, and audit analytics. A single synthetic dataset is used for evaluation. It contains 186,000 finance-process records from six subsidiaries over 2022-2025, including accounts payable invoices, payments, general-ledger journals, accounts receivable receipts, and bank-statement lines. The dataset includes entity relationships, monetary values, approval paths, tax attributes, banking markers, exception labels, fraud-rule triggers, and control-failure outcomes. Because the dataset is synthetic, reported results demonstrate controlled internal consistency rather than production validation. Against a production-clone upper bound, static masking, rules-only synthesis, conditional tabular generative synthesis, and a hybrid baseline, SEQ-FCT achieved 0.932 reconciliation F1, 0.887 fraud-trigger recall, 0.914 control-failure F1, and an estimated leakage-risk score of 0.018. The analysis indicates that financial process behavior can be preserved more reliably when masking, synthetic data, and governance checks are evaluated as a single release pipeline instead of independent utilities.  \nIndex Terms-ERP systems, financial automation, data masking, synthetic data, audit analytics, reconciliation, fraud detection, privacy governance  \nI. INTRODUCTION  \nEnterprise resource planning systems have become the accounting memory of large organizations. Accounts payable, accounts receivable, general ledger, treasury, tax, procurement, and consolidation processes leave dense trails of documents, approvals, clearing events, master-data changes, and exception outcomes. These records are necessary for automated reconciliation, fraud-rule validation, regression testing after configuration changes, and audit-analytics procedures. However, the same records frequently contain vendor banking details, employee identifiers, customer addresses, tax numbers, pricing terms, dispute notes, and subsidiary-level control evidence. Recent work on secure data provisioning for ERP quality environments describes the practical tension between realistic non-production data and financial-data confidentiality [1] . Related research on automated financial consolidation and reporting highlights that reconciliation logic depends on relationships among  \nledgers, intercompany balances, currencies, and posting periods rather than isolated fields [2] . Accounts-payable fraud-detection research also shows that duplicate invoices, payment timing, vendor-risk attributes, and approval patterns must remain coherent for controls to be tested in a meaningful way [3] .  \nThe operational problem is more difficult than ordinary data anonymization. A quality ERP client must support endto-end process execution. Payment proposals require vendorbank tokens that are consistent across invoices, bank files, payment batches, and audit logs. Tax checks require realistic combinations of company code, jurisdiction, material, exemption reason, and document type. Reconciliation tests require debit and credit values to retain balancing relationships after transformation. Fraud and segregation-ofduties rules require user-role patterns, approval chains, posting thresholds, and exception status to behave as they would in production. If data are over-masked, automated tests pass for the wrong reason because business rules are no longer exercised. If data are u","cbCaiegzJ0s4Kjhc","https://ap.wps.com/l/cbCaiegzJ0s4Kjhc","pdf",514486,1,9,"English","en",105,"# Introduction\n## Challenges of using ERP data in quality environments\n## Limitations of common data protection patterns\n## SEQ-FCT design principles","[{\"question\":\"What problem does SEQ-FCT address in financial control testing?\",\"answer\":\"It addresses the need for realistic ERP process data in quality environments while preventing disclosure of personal, supplier, banking, and other sensitive production records.\"},{\"question\":\"How does SEQ-FCT maintain referential integrity across ERP processes?\",\"answer\":\"SEQ-FCT deterministically tokenizes entity identifiers and coordinates transformations so relationships needed for payments, taxes, and reconciliation remain valid after masking or generation.\"},{\"question\":\"What does the evaluation dataset contain and how was it used?\",\"answer\":\"The evaluation uses a single synthetic dataset with 186,000 finance-process records from six subsidiaries across 2022–2025, including AP invoices, payments, general-ledger journals, AR receipts, and bank-statement lines, enabling controlled internal-consistency testing of reconciliation, fraud-rule, and control-failure 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problem does SEQ-FCT address in financial control testing?","Question",{"text":75,"@type":76},"It addresses the need for realistic ERP process data in quality environments while preventing disclosure of personal, supplier, banking, and other sensitive production records.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SEQ-FCT maintain referential integrity across ERP processes?",{"text":80,"@type":76},"SEQ-FCT deterministically tokenizes entity identifiers and coordinates transformations so relationships needed for payments, taxes, and reconciliation remain valid after masking or generation.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the evaluation dataset contain and how was it used?",{"text":84,"@type":76},"The evaluation uses a single synthetic dataset with 186,000 finance-process records from six subsidiaries across 2022–2025, including AP invoices, payments, general-ledger journals, AR receipts, and bank-statement lines, enabling controlled 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