International Journal For Multidisciplinary Research

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Metadata-Driven Pipeline Design for Automated Tax Fraud Detection

Author(s) Ravi Kiran Alluri
Country United States
Abstract The growing complexity and volume of tax-related data have significantly challenged traditional fraud detection methods in governmental and enterprise financial systems. Manual analysis or static rule-based systems often fail to detect emerging fraud patterns and cannot scale to match the dynamic nature of modern tax evasion techniques. This paper presents a metadata-driven pipeline architecture for automating tax fraud detection, enabling real-time anomaly identification and intelligent orchestration of fraud detection workflows. The proposed architecture leverages structured metadata—such as schema information, data quality metrics, lineage, and usage logs—to dynamically configure, monitor, and adapt the data pipeline without manual intervention.
The system is designed to handle a wide array of data sources, including financial transactions, income declarations, invoice submissions, and tax return filings, and uses metadata to enforce data consistency, compliance checks, and behavioral anomaly detection. At the core of the architecture lies a metadata catalog that stores dynamic rules, schema mappings, fraud indicators, and transformation logs, which inform downstream machine learning models and pattern-matching engines in a plug-and-play fashion. This allows data engineers and analysts to trace suspicious behavior through lineage and correlation, while auditors can verify the steps taken by the automated pipeline.
A prototype was implemented using open-source technologies like Apache Atlas for metadata management, Apache NiFi for pipeline orchestration, and Spark MLlib for fraud pattern analysis. Results from multiple case studies involving synthetic and historical tax datasets demonstrate improved precision and recall compared to static fraud detection systems, faster development cycles, and enhanced traceability. This paper provides a methodological foundation for integrating metadata-driven designs into fraud analytics pipelines, significantly improving responsiveness and adaptability in tax fraud prevention mechanisms.
The proposed approach is particularly relevant in compliance-heavy environments such as national revenue services, multinational corporations, and auditing firms, where scalability and auditability are paramount. With the increasing availability of rich metadata and the advancement of orchestration tools, this architecture represents a forward-thinking blueprint for building resilient and adaptive fraud detection systems. The paper concludes by discussing future enhancements, such as semantic metadata modeling, real-time policy-driven transformations, and integration with distributed ledger technologies to strengthen data provenance and fraud detection capabilities further.
Keywords Metadata-driven architecture; tax fraud detection; automated data pipelines; data lineage; fraud analytics; data orchestration; Apache Atlas; data governance; machine learning; schema mapping; financial compliance; anomaly detection; NiFi; metadata catalog; pipeline automation.
Field Engineering
Published In Volume 2, Issue 2, March-April 2020
Published On 2020-03-04
DOI https://doi.org/10.36948/ijfmr.2020.v02i02.53078
Short DOI https://doi.org/
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E-ISSN 2582-2160
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