Data Quality Frameworks for Fraud Detection in Financial Reporting Pipelines

Transparency, regulatory compliance, and trust in financial ecosystems depend on the integrity of financial reporting pipelines. Financial reporting fraud remains a widespread problem with serious repercussions for markets and stakeholders. Ensuring data quality at every stage is crucial for operational efficiency and successful fraud detection, as businesses depend increasingly on automated data pipelines for real-time financial reporting. With an emphasis on fraud detection capabilities, this paper investigates the development and application of comprehensive data quality frameworks suited to financial reporting pipelines. Post-hoc audits and manual checks are frequently the mainstays of traditional fraud detection methods, which are inadequate for managing large volumes of financial data at high speeds. The credibility of analytics and regulatory reporting can also be weakened by missing, erroneous, and inconsistent data in reporting systems, which can conceal fraudulent activity or produce false positives. Therefore, a strong data quality framework must incorporate domain-specific integrity constraints, anomaly detection logic, lineage tracing mechanisms, and enforce standard data validation rules. In line with anti-fraud goals, this study suggests a structured framework for data quality that combines rule-based, statistical, and metadata-driven validation procedures. Five essential pillars—completeness, accuracy, consistency, timeliness, and integrity—are incorporated into the framework presented in this paper. To identify questionable anomalies and deviations early, each pillar is mapped to particular validation mechanisms, including cross-ledger balancing, reference reconciliation, threshold-based monitoring, schema enforcement, and duplication checks. By incorporating these dimensions into ETL pipelines, organizations can proactively evaluate and score the quality of incoming data and flag records that might point to fraudulent manipulation, such as manipulated ledger entries, underreported liabilities, or revenue misstatements. The framework is integrated into a modular architecture that guarantees practical applicability with contemporary cloud-native data platforms and legacy systems. It uses tools like SQL-based integrity rules to identify transaction irregularities, Apache Atlas to track lineage and transformations, and Apache NiFi to orchestrate validation workflows. Financial controllers and compliance officers are also given access to real-time metrics and dashboards to monitor fraud risk indicators and data quality scores. This study uses a synthetic financial dataset enhanced with known fraud scenarios to assess the efficacy of the suggested framework. The analysis shows that while low-quality data introduces a lot of noise and lowers model reliability, high data quality scores are strongly correlated with fewer false positives in fraud detection models. The study also demonstrates how incorporating data quality validation early in the pipeline lifecycle enhances reporting output trust and speeds up the identification of fraudulent trends. This framework aids in the creation of safe, audit-ready, and compliant financial reporting pipelines by coordinating data quality assurance with fraud detection objectives. Additionally, it fills a significant void in managing financial data, where data quality is frequently viewed as an operational issue rather than a security or compliance requirement. The research’s conclusions apply to financial institutions, regulators, auditors, and tech companies who want to improve financial reporting systems’ resistance to fraud. This paper argues for a paradigm change in which data quality frameworks are essential to financial reporting fraud detection rather than being merely incidental. Adopting such frameworks will be crucial to creating robust, transparent, and reliable financial ecosystems as the financial sector accelerates digital transformation. Future research could build on this work by incorporating machine learning methods into the data quality scoring process, enabling even more automation and accuracy in fraud detection workflows.