Compliance-Aware Feature Engineering for Predictive Analytics in Financial Cloud Data Architectures

  • Authors

    • Dr. Anita Verma Associate Professor, Banaras Hindu University, India. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2018PII4N8B

    Published 09-07-2018

  • Compliance-Aware Feature Engineering, Predictive Analytics, Financial Data, Cloud Data Architecture, Regulatory Compliance, GDPR, Data Governance, Machine Learning Pipelines, Data Privacy, Feature Selection, Responsible AI, Federated Learning, Financial Services

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    A. Verma, “Compliance-Aware Feature Engineering for Predictive Analytics in Financial Cloud Data Architectures”, IJDEIC, vol. 1, no. 2, pp. 01–11, Sep. 2018, doi: 10.67228/30715717/IJDEIC-2018PII4N8B.

  • Abstract

    As financial institutions increasingly migrate to cloud-based infrastructures, the demand for robust, compliant predictive analytics solutions has grown substantially. Feature engineering—a cornerstone of machine learning—poses significant compliance risks when it inadvertently exposes sensitive or regulated information. In this paper, we introduce a compliance-aware feature engineering framework tailored for predictive analytics in financial cloud data architectures. Our approach integrates regulatory requirements directly into the feature engineering pipeline by leveraging data classification, metadata tracking, and automated policy enforcement. We present an architecture compatible with major cloud providers and demonstrate its effectiveness through a case study using a simulated financial dataset. Our results show that compliance-aware feature pipelines can maintain model performance while ensuring adherence to legal and regulatory standards. This work bridges the gap between regulatory compliance and scalable ML pipeline design, paving the way for more trustworthy and auditable AI systems in finance.

  • References

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