Integrated Information Management Systems (LIMS) in Banking:
1. Niranjana Raghunathan, GSK, Other, United States
U.S. banking institutions operate at the intersection of high-volume financial transactions, stringent regulatory oversight, and increasing public-sector dependency for benefit disbursement and compliance enforcement. While Laboratory Information Management Systems (LIMS) originate in scientific domains, their core architectural principles centralized data governance, workflow automation, and auditability are increasingly applicable to banking technology through analogous integrated information management platforms. This paper examines the role of LIMS-inspired systems in U.S. banking, with particular emphasis on interoperability with public-sector entities including the Social Security Administration (SSA), Department of the Treasury, and Internal Revenue Service (IRS). Drawing on regulatory reports and empirical statistics, we hypothesize that such systems measurably reduce compliance costs, improper payments, and operational risk, thereby strengthening financial system resilience. The findings position integrated information management as critical national banking infrastructure.
integrated information managent LIMS inspired architecture banking compliance Data Provenance financial stability
LIMS-inspired integrated
information management systems represent a critical evolution in banking
technology. By enabling secure, auditable interoperability with public-sector
institutions, these platforms reduce fraud, improve regulatory efficiency, and
strengthen U.S. financial system resilience. As banking continues to serve as
execution infrastructure for national policy, such systems should be considered
foundational components of modern financial architecture.
1. 1. U.S. Treasury Inspector General for Tax Administration. (2022). Interim results of the administration of pandemic-related relief programs. Department of the Treasury. 2. Financial Stability Board. (2020). Effective practices for cyber incident re-sponse and recovery. Bank for International Settlements. 3. Basel Committee on Banking Supervision. (2021). Sound practices: Impli-cations of fintech developments for banks and bank supervisors. Bank for International Settlements. 4. Arner, D. W., Barberis, J., & Buckley, R. P. (2017). FinTech and RegTech in financial regulation. Northwestern J. Int’l L. & Bus, 37(3), 371–413. 5. Deloitte. (2023). 2023 global regulatory outlook: Navigating complexity and change. Deloitte Insights. 6. GAO 24106927: "Improper Payments: Information on Agencies' Fiscal Year 2024 Estimates". 7. OECD. (2021). Digital government and data governance. 8. SSA. (2023). Annual statistical supplement. 9. Basel Committee on Banking Supervision. (2013). Principles for effective risk data aggregation and risk reporting (BCBS 239). Bank for International Settlements. 10. Gozman, D., Liebenau, J., & Mangan, M. (2020). The innovation mecha-nisms of fintech start-ups: Insights from SWIFT’s innotribe competition. Journal of Management Information Systems, 35(1), 145–179. 11. Organisation for Economic Co-operation and Development (OECD). (2021). Public sector data governance: Towards trusted and interoperable data sharing. OECD Publishing. 12. Sarkar, S., & Shetty, S. (2017). Data governance and regulatory compliance in financial services. Journal of Risk Management in Financial Institutions, 10(4), 362–374. 13. U.S. Department of Labor. (2022). Unemployment insurance improper payments report. Employment and Training Administration. 14. U.S. Government Accountability Office. (2024). Improper payments: Fiscal year 2023 estimates and agency actions. GAO. 15. U.S. Internal Revenue Service. (2024). Data book: Processing statistics and refund volumes. Department of the Treasury. 16. McDowall, R. D. (2017). Data integrity and data governance in regulated laboratories. LCGC Europe, 30(9), 500–506. 17. U.S. Food and Drug Administration. (2018). Data integrity and compliance with drug CGMP. FDA Guidance for Industry.
Padmanaban Vartharajan & Niranjana Raghunathan conceived the study, de-veloped the architectural framework and quantitative methodology, performed the literature analysis and systems synthesis, and wrote the manuscript.
This research received no external funding.
The authors declare that there are no conflicts of interest associated with this study.
The author acknowledges the many colleagues, engineers, scientists, and quality professionals across pharmaceutical R&D, manufacturing, and laboratory operations whose work over many years informed the practical perspectives presented in this study. The views expressed are those of the author and do not necessarily reflect those of any current or former employers or collaborators. Micheal Piecuch, Sruti Parikh, Rami Sabbah for editing
No clinical
trials or human subject studies were conducted as part of this work. The study
is based on publicly available literature and de-identified, aggregated
industrial reference data that cannot be shared due to confidentiality and
contractual restrictions. All conceptual frameworks, metric definitions, and
methodological descriptions necessary to reproduce the analytical approach are
provided within the article.