Insurance Regulatory Compliance: Using the Guidewire team Solutions to Promote Openness and Flexibility

Authors

  • Ravi Teja Madhala Senior Software Developer Analyst at Mercury Insurance Services, LLC, USA Author

Keywords:

Regulatory compliance, insurance technology, Guidewire solutions, evolving regulations, digital transformation

Abstract

In the insurance sector a regulatory compliance is intricate & the ever-changing. Guidewire's BillingCenters, PolicyCenters & ClaimCenters are the solutions to assist insurers in overcoming these obstacles. These solutions is guarantee a compliance with the most recent rules, automate crucial procedures & reduces the mistakes. Insurers may be reduce the risk of non-compliance by keeping the consistent records & making them readily auditable. Thanks to adjustable workflows & the process modifications. Guidewire's technology helps insurers swiftly adjust to regulatory changes without completely the redesigning key systems. For regulators, auditors, & insurers, seamless data integration offers actual time insights that enhances transparency & foster the confidence. This simplified method guarantees that claims and policies are handled effectively & equitably, which improves customer experiences in addition to the supporting compliance. Guidewire's adaptable & the effective solutions have been grown essential as laws by pertaining to consumer rights, data protection, and financial transparency change. Insurers may be concentrate on fostering responsibility & trust while confidently and nimbly negotiating the regulatory environment.

References

1. VanderLinden, S. L., Millie, S. M., Anderson, N., & Chishti, S. (2018). The insurtech book: The insurance technology handbook for investors, entrepreneurs and fintech visionaries. John Wiley & Sons.

2. Kaniyar, S., Peters, P., & Vogelgesang, U. (2015). Transitioning to standard software: Lessons from ERP pioneers. McKinsey & Company-Business Technology Office, 1-6.

3. Heep-Altiner, M., Mullins, M., & Rohlfs, T. (2018). Solvency II in the Insurance Industry. Springer International Publishing.

4. Jeyakumar, N. (2017). Analysis of the Digital Direct-to-Customer channel in Insurance (Doctoral dissertation, Massachusetts Institute of Technology).

5. Bec, J., Phipps, J. E., Gorpas, D., Ma, D., Fatakdawala, H., Margulies, K. B., ... & Marcu, L. (2017). In vivo label-free structural and biochemical imaging of coronary arteries using an integrated ultrasound and multispectral fluorescence lifetime catheter system. Scientific reports, 7(1), 8960.

6. Hobert, K. A., Woodbridge, M., Mariano, J., & Tay, G. (2017). Magic quadrant for content services platforms. Gartner, Stamford, CT, available at: https://b2bsalescafe. files. wordpress. com/2017/11/magic-quadrant-for-content-services-platforms-oct-2017. pdf (accessed 15 October 2022).

7. Sanborn, T. A., Tcheng, J. E., Anderson, H. V., Chambers, C. E., Cheatham, S. L., DeCaro, M. V., ... & Windle, J. R. (2014). ACC/AHA/SCAI 2014 health policy statement on structured reporting for the cardiac catheterization laboratory: a report of the American College of Cardiology Clinical Quality Committee. Circulation, 129(24), 2578-2609.

8. Schudy, J. B. (2018). How Office Based Procedural Suites Are Impacting the Health Care Industry. Central Michigan University.

9. Liu, C. Y., Farahani, K., Lu, D. S., Duckwiler, G., & Oppelt, A. (2000). Safety of MRI‐guided endovascular guidewire applications. Journal of Magnetic Resonance Imaging, 12(1), 75-78.

10. Wang, P., Chen, T., Zhu, Y., Zhang, W., Zhou, S. K., & Comaniciu, D. (2009, June). Robust guidewire tracking in fluoroscopy. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 691-698). IEEE.

11. Lenoir, J., Cotin, S., Duriez, C., & Neumann, P. (2006). Interactive physically-based simulation of catheter and guidewire. Computers & Graphics, 30(3), 416-422.

12. Aarnoudse, W., van den Berg, P., van de Vosse, F., Geven, M., Rutten, M., Van Turnhout, M., ... & Pijls, N. (2004). Myocardial resistance assessed by guidewire‐based pressure‐temperature measurement: in vitro validation. Catheterization and cardiovascular interventions, 62(1), 56-63.

13. Baert, S. A., Viergever, M. A., & Niessen, W. J. (2003). Guide-wire tracking during endovascular interventions. IEEE Transactions on Medical Imaging, 22(8), 965-972.

14. Tse, F., Yuan, Y., Moayyedi, P., & Leontiadis, G. I. (2013). Guide wire-assisted cannulation for the prevention of post-ERCP pancreatitis: a systematic review and meta-analysis. Endoscopy, 45(08), 605-618.

15. Cheung, J., Tsoi, K. K., Quan, W. L., Lau, J. Y., & Sung, J. J. (2009). Guidewire versus conventional contrast cannulation of the common bile duct for the prevention of post-ERCP pancreatitis: a systematic review and meta-analysis. Gastrointestinal endoscopy, 70(6), 1211-1219.

16. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).

17. Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).

18. Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).

19. Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).

20. Naresh Dulam. The Shift to Cloud-Native Data Analytics: AWS, Azure, and Google Cloud Discussing the Growing Trend of Cloud-Native Big Data Processing Solutions. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Feb. 2015, pp. 28-48

21. Naresh Dulam. DataOps: Streamlining Data Management for Big Data and Analytics . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Oct. 2016, pp. 28-50

22. Naresh Dulam. Machine Learning on Kubernetes: Scaling AI Workloads . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Sept. 2016, pp. 50-70

23. Naresh Dulam. Data Lakes Vs Data Warehouses: What’s Right for Your Business?. Distributed Learning and Broad Applications in Scientific Research, vol. 2, Nov. 2016, pp. 71-94

24. Naresh Dulam, et al. Kubernetes Gains Traction: Orchestrating Data Workloads. Distributed Learning and Broad Applications in Scientific Research, vol. 3, May 2017, pp. 69-93

25. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.

26. Komandla, Vineela. "Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction." Available at SSRN 4983012 (2018)

Published

26-08-2019

How to Cite

[1]
Ravi Teja Madhala, “Insurance Regulatory Compliance: Using the Guidewire team Solutions to Promote Openness and Flexibility”, Distrib. Learn. Broad Appl. Sci. Res., vol. 5, pp. 1–17, Aug. 2019, Accessed: Mar. 14, 2025. [Online]. Available: https://dlbasr.org/index.php/publication/article/view/46