Architecture Patterns for Disclosure Applicability Detection in Regulated Financial Advisory Platforms

  • Authors

    • Abhiram Potharaju Senior Software Engineer, Wells Fargo–Trust & Wealth Technology, USA. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2022PII2J6Z

    Published 12-29-2022

  • Disclosure Detection, Content Screening, Compliance Automation, OCR, Rules Engine, Financial Advisory Platforms, RegTech, Non-deposit Investment Products

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    Potharaju, A. (2022). Architecture Patterns for Disclosure Applicability Detection in Regulated Financial Advisory Platforms. International Journal of Commerce, Finance and Digital Economy, 5(2), 01-32. https://doi.org/10.67228/3071642X/IJCFDE-2022PII2J6Z

  • Abstract

    Financial advisory institutions are faced with stringent regulatory requirements for accurate, client-specific disclosures but manual review processes are characterized by high operational costs, long onboarding times, inconsistent applicability determinations and increased compliance risk. To tackle these issues, this paper proposes a unified enterprise architecture for Disclosure Applicability Detection (DAD) that automates compliance decision making in RegTech environments. The layered architecture leverages Optical Character Recognition (OCR) to convert unstructured application forms to searchable text, intelligent content screening for extraction of critical regulatory and financial metrics and a metadata driven business rule engine to dynamically evaluate customer profile, product characteristics and jurisdictional policies. The system dynamically orchestrates disclosure recommendations in real-time within advisory workflows, instead of relying on static checklists. Centralized audit logging, exception management, and real-time reporting provide end-to-end transparency. Conceptual evaluations demonstrate that the proposed architecture greatly speeds up document processing, improves disclosure accuracy and enables rapid policy updates without code refactoring, providing a scalable template for modern financial compliance governance.

  • References

    [1] Arner, D. W., Barberis, J., & Buckley, R. P. (2017). FinTech, RegTech, and the role of regulation. Northwestern Journal of International Law & Business, 37(3), 371–413.

    [2] Basel Committee on Banking Supervision. (2021). Principles for operational resilience. Bank for International Settlements.

    [3] Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213–238.

    [4] Chowdary Aluri, G. N., & Mupparapu, H. K. (2021). Spring Batch Framework Patterns for Large-Scale Data Processing in Cloud-Native Microservices: A GCP Deployment Study. International Journal of AI, BigData, Computational and Management Studies, 2(4), 130-142. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I4P113

    [5] Deloitte. (2020). RegTech and the role of technology in regulatory transformation. Deloitte Insights.

    [6] Financial Action Task Force. (2021). Opportunities and challenges of new technologies for AML/CFT. FATF.

    [7] Gai, K., Qiu, M., & Sun, X. (2018). A survey on FinTech. Journal of Network and Computer Applications, 103, 262–273. https://doi.org/10.1016/j.jnca.2017.10.011

    [8] Gartner. (2020). Market guide for regulatory technology (RegTech). Gartner Research.

    [9] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

    [10] Hammer, M. (2015). What is business process management? In J. vom Brocke & M. Rosemann (Eds.), Handbook on business process management 1 (pp. 3–16). Springer.

    [11] International Organization for Standardization. (2018). ISO 31000:2018 risk management—Guidelines. ISO.

    [12] International Organization for Standardization. (2021). ISO/IEC 27001:2021 information security management systems—Requirements. ISO.

    [13] Inmon, W. H. (2016). Building the data warehouse (5th ed.). Wiley.

    [14] Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–705.

    [15] Laudon, K. C., & Laudon, J. P. (2020). Management information systems: Managing the digital firm (16th ed.). Pearson.

    [16] Mallempati, A., & Jaladi, D. S. (2021). A Scalable Master Data Management Architecture for Enterprise Data Integration and Governance in Full-Stack Application Environments. International Journal of Emerging Research in Engineering and Technology, 2(1), 101-110. https://doi.org/10.63282/3050-922X.IJERET-V2I1P111

    [17] Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (NIST Special Publication 800-145). National Institute of Standards and Technology.

    [18] Microsoft Corporation. (2021). Azure architecture center: Cloud architecture patterns for enterprise applications. Microsoft.

    [19] Mupparapu, H. K., & Movva, C. K. (2020). Serverless Migration Patterns for Compliance-Critical Financial Applications on Microsoft Azure. International Journal of Emerging Research in Engineering and Technology, 1(2), 85-96. https://doi.org/10.63282/3050-922X.IJERET-V1I2P111

    [20] National Institute of Standards and Technology. (2020). Artificial intelligence risk management framework: Initial draft. U.S. Department of Commerce.

    [21] Object Management Group. (2019). Decision Model and Notation (DMN), Version 1.3. Object Management Group.

    [22] Object Management Group. (2014). Business Process Model and Notation (BPMN), Version 2.0.2. Object Management Group.

    [23] O'Leary, D. E. (2019). Artificial intelligence and big data in accounting and finance: Opportunities and challenges. Intelligent Systems in Accounting, Finance and Management, 26(2), 89–95.

    [24] Otto, B. (2015). Quality and value of the data resource in large enterprises. Information Systems Management, 32(3), 234–251.

    [25] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.

    [26] Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence. MIT Sloan Management Review, 59(1), 1–17.

    [27] Schmarzo, B. (2020). The economics of data, analytics, and digital transformation. Wiley.

    [28] Sicular, S. (2020). Artificial intelligence and machine learning in financial services. Gartner Research.

    [29] van der Aalst, W. M. P. (2016). Process mining: Data science in action (2nd ed.). Springer.

    [30] Weske, M. (2019). Business process management: Concepts, languages, architectures (3rd ed.). Springer.

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