Advanced Data Quality Assurance Techniques in Financial Data Processing: Beyond the Basics

Authors: Srujana Manigonda

DOI: https://doi.org/10.5281/zenodo.14535660

Short DOI: https://doi.org/g8wjqn

Country: USA

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Abstract: In the rapidly evolving financial industry, ensuring high data quality is critical for accurate decision-making, regulatory compliance, and operational efficiency. Traditional data quality assurance (DQA) methods often fall short when handling large, complex, and real-time financial datasets. This paper explores advanced DQA techniques that go beyond conventional methods, emphasizing scalable, automated, and intelligent solutions tailored for financial data processing environments. It highlights methodologies such as predictive data validation, anomaly detection using machine learning, data lineage tracking, and real-time data monitoring. A detailed case study demonstrates how these techniques can mitigate risks, reduce operational costs, and enhance trust in financial data reporting. The paper concludes with a discussion on emerging trends, best practices, and future research directions in the field of data quality assurance in financial services.

Keywords: Data Quality Assurance (DQA), Financial Data Processing, Anomaly Detection, Data Integrity, Real-Time Data Monitoring, Predictive Analytics, Machine Learning in Data Quality, Data Validation, Financial Compliance, Automated Data Processing, Data Governance, Data Accuracy, Data Cleansing, Data Reliability, Data Quality Frameworks, Regulatory Compliance, Operational Risk Management, Data Error Detection, Advanced Analytics, Financial Technology (FinTech)

Paper Id: 231877

Published On: 2022-06-07

Published In: Volume 10, Issue 3, May-June 2022

Cite This: Advanced Data Quality Assurance Techniques in Financial Data Processing: Beyond the Basics - Srujana Manigonda - IJIRMPS Volume 10, Issue 3, May-June 2022. DOI 10.5281/zenodo.14535660

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