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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper ID:IJSREM62231

Dynamic Rate Publishing Workflows: Automation and Compliance

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62231

Volume:

10 |

4 |

April 2026

Pages:

1-17

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62231

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SS

Shreyansh Sharma

Independent Researcher

Jersey City, NJ

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Abstract

The paper discusses automated rate publishing systems in various regulated industries, technical architectures, compliance requirements, and implementation strategies. We introduce a five-layer architecture that has used compliance-by-design principles, which has been empirically validated using three case studies in the industry. One of the world hotel chains recorded 99.4% cut in the publishing cycle time with revenues per available room increasing by 7.3%. A property and casualty insurer saved 50.6% in the time required to file and eradicated compliance violations. Nevertheless, such challenges remain as regulatory complexity, old integration, data quality dependencies, and the threat of algorithm bias. Technology, organizational change and governance must be balanced in the achievement of success, and a staged deployment can help balance these three factors effectively to control the risk and develop the capability. The study provides frameworks and practical provisions on how organizations can deploy rate publishing automation and remain in regulatory compliance, and future research directions in AI-driven compliance, blockchain audit trails, and industry standards.

KEYWORDS

Keywords: Rate publishing workflow automation dynamic pricing revenue management RegTech API integration audit trails insurance rate filing

Cite This Article

Shreyansh Sharma, "Dynamic Rate Publishing Workflows: Automation and Compliance," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-17, April 2026. https://doi.org/10.55041/IJSREM62231

Full Article/Conclusion

It has researched the important cross-function of automation in rate publishing and regulatory compliance in various sectors, showing that any form of successful digital transformation in pricing processes must be carefully maintained between technological capacity and regulatory compliance. The results indicate that companies that install well-designed automated rate publishing frameworks attain transformational operational enhancement and sustain and frequently improve compliance posture. The shift in the method of managing pricing within the distribution channel is not merely an incremental change, but a fundamental paradigm shifts as manual and batch based processing systems are replaced by automated and real time processing systems. These two competitive advantages of speed and accuracy give the benefit beyond operational efficiency to include revenue maximization, responsiveness to market, and reduction of risks. The regulatory landscape analysis indicates that compliance requirements differ greatly in different industries with insurance and financial services requiring high levels of rating filing and disclosure, whereas the hospitality and telecommunications industries have other, yet equally important, levels of transparency and fairness requirements. These differences notwithstanding, there are common themes, among them the need to have detailed documentation, give warning of rate changes, ban on the use of discriminatory prices, and the need to have audit trails. Automated systems meet these needs in a more rigorous and holistic way than a manual process, but must be explicitly programmed with regulatory regulations, and updated continuously as new regulations are developed. The technical architecture provided in this paper has shown that compliance controls should not be an after-thought in the system design. The five-layer design data, business logic, integration, distribution, and monitoring/compliance offers the separation of concerns and at the same time allows the cohesive operation. A compliance engine is an important control point, which enforces libraries of regulatory rules, multi-phase validation processes, exception management, and automatic production of documentation. The ability of audit trails should be irrevocable and detailed to provide the history of version rates, approvals, distribution records, verification check outcomes and configuration variations.

References

[1] S. Tatipamula, “The Evolution of AI Workflow Automation: From Rules to Adaptive Intelligence,” International Journal on Science and Technology, vol. 16, no. 2, Apr. 2025, doi: https://doi.org/10.71097/ijsat.v16.i2.3531. [2] S. Ravichandran, “Enhancing workflow automation in international business: how Microsoft Power Automate drives efficiency and ensures compliance,” 2025, doi: http://www.theseus.fi/handle/10024/900182. [3] M. Pala, F. Edum-Fotwe, K. Ruikar, C. Peters, and N. Doughty, “Implementing commercial information exchange: a construction supply chain case study,” Construction Management and Economics, vol. 34, no. 12, pp. 898–918, Aug. 2016, doi: https://doi.org/10.1080/01446193.2016.1211718. [4] I. Bakshi, “Impact of Online Distribution Systems of Room Reservations on Hotel Revenue: A study of Selected Hotels of Delhi (NCR),” Tuijin Jishu/Journal of Propulsion Technology, vol. 44, no. 4, pp. 4602–4612, Nov. 2023, doi: https://doi.org/10.52783/tjjpt.v44.i4.1758. [5] A. M. Bandalouski, M. Y. Kovalyov, E. Pesch, and S. A. Tarim, “An overview of revenue management and dynamic pricing models in hotel business,” RAIRO - Operations Research, vol. 52, no. 1, pp. 119–141, Jan. 2018, doi: https://doi.org/10.1051/ro/2018001. [6] D. Singireddy, “The Evolution of Data in Financial Systems: From Batch to Real-Time,” Journal of Computer Science and Technology Studies, vol. 7, no. 8, pp. 596–604, 2025, doi: https://doi.org/10.32996/jcsts.2025.7.8.69. [7] O. O. Fagbore, J. C. Ogeawuchi, O. Ilori, N. J. Isibor, A. Odetunde, and B. I. Adekunle, “Optimizing Client Onboarding Efficiency Using Document Automation and Data-Driven Risk Profiling Models,” Journal of Frontiers in Multidisciplinary Research, vol. 3, no. 1, pp. 241–257, 2022, doi: https://doi.org/10.54660/.jfmr.2022.3.1.241-257. [8] A. Bhartariya, S. Singh, and A. Bharti, “FRAMEWORK FOR DATA INTEGRATION IN CROSS- DOMAIN PERVASIVE ENVIRONMENTS: LIGHTWEIGHT AND SECURE RESTFUL API,” Journal of Theoretical and Applied Information Technology, vol. 103, no. 18, 2025, Accessed: Feb. 15, 2026. [Online]. Available: https://www.jatit.org/volumes/Vol103No18/10Vol103No18.pdf [9] K. Shivashankar, Al, and A. Martini, “Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review,” arXiv.org, 2025. https://arxiv.org/abs/2504.11079 (accessed May 15, 2025). [10] A. Haleem, M. Javaid, R. P. Singh, S. Rab, and R. Suman, “Hyperautomation for the enhancement of automation in industries,” Sensors International, vol. 2, no. 1, p. 100124, 2021, doi: https://doi.org/10.1016/j.sintl.2021.100124.

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Publication Date

4/27/2026

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Review Rounds: 2

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