Making the Switch to Guidewire Cloud and SaaS Services to Optimize P&C Insurance Operations
Keywords:
Cloud Adoption, SaaS Solutions, P&C Insurance, Cloud Deployment, Change ManagementAbstract
The insurance business are changed as a result of the cloud adoption, particularly in the Property & Casualty sector, where several insurers are using the products like Guidewire Cloud. Significant advantages of this changes include increased scalabilities, agilities & the speed of inventions. Insurance companies may be expedite upgrades, lessen their reliance on the on-premises technologies & concentrate more on client demands and corporate expansions by migrating to the cloud. Adoptions of the cloud has many benefits, but there are drawbacks as well, such as the issues with a data securities, compliances & the migration difficulties. Guidewire Cloud provides reduces the maintenances overhead, faster updates & a more predictable pricing model than the on-premises solutions. Cloud solutions allows us to insurers to adapt & the innovate more quickly than on-premises installations, which sometimes involve huge IT expenditures & lengthier timetables. Examples from the real life demonstrates the effects of cloud adoption. By adopting the Guidewire Cloud, insurers have been improved customers satisfaction & the efficiency, releasing new products faster & managing on demand swings with simplicity. The processing of claims has also been accelerated by automations & the integration capabilities. These insurers maintain their competitiveness in a digital first market thanks to a frequent upgrades & the little low time. The long-term advantages of flexibilities, scalabilities & the efficiency makes Guidewire Cloud an excellent option for Property & Casualty insurers wishing to modernize & the future-proof their operations, despite the fact that moving to the cloud may be difficult.
References
1. Bommadevara, N., Del Miglio, A., & Jansen, S. (2018). Cloud adoption to accelerate IT modernization. McKinsey Digital, 15.
2. Wu, W. W. (2011). Developing an explorative model for SaaS adoption. Expert systems with applications, 38(12), 15057-15064.
3. Palos-Sanchez, P. R., Arenas-Marquez, F. J., & Aguayo-Camacho, M. (2017). Cloud computing (SaaS) adoption as a strategic technology: Results of an empirical study. Mobile Information Systems, 2017(1), 2536040.
4. Yang, Z., Sun, J., Zhang, Y., & Wang, Y. (2015). Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model. Computers in Human Behavior, 45, 254-264.
5. Wu, W. W., Lan, L. W., & Lee, Y. T. (2011). Exploring decisive factors affecting an organization's SaaS adoption: A case study. International Journal of Information Management, 31(6), 556-563.
6. Safari, F., Safari, N., & Hasanzadeh, A. (2015). The adoption of software-as-a-service (SaaS): ranking the determinants. Journal of Enterprise Information Management, 28(3), 400-422.
7. Janssen, M., & Joha, A. (2011). Challenges for adopting cloud-based software as a service (saas) in the public sector.
8. Johansson, B., & Ruivo, P. (2013). Exploring factors for adopting ERP as SaaS. Procedia Technology, 9, 94-99.
9. Seethamraju, R. (2015). Adoption of software as a service (SaaS) enterprise resource planning (ERP) systems in small and medium sized enterprises (SMEs). Information systems frontiers, 17, 475-492.
10. Tan, C., Liu, K., & Sun, L. (2013). A design of evaluation method for SaaS in cloud computing. journal of Industrial Engineering and Management, 6(1).
11. Lewandowski, J., Salako, A. O., & Garcia-Perez, A. (2013, September). SaaS enterprise resource planning systems: Challenges of their adoption in SMEs. In 2013 IEEE 10th International Conference on e-Business Engineering (pp. 56-61). IEEE.
12. Seethamraju, R. (2013, June). Determinants of SaaS ERP Systems Adoption. In PACIS (p. 244).
13. Branco Jr, T., de Sá-Soares, F., & Rivero, A. L. (2017). Key issues for the successful adoption of cloud computing. Procedia computer science, 121, 115-122.
14. Smith, A., Bhogal, J., & Sharma, M. (2014, August). Cloud computing: adoption considerations for business and education. In 2014 international conference on future internet of things and cloud (pp. 302-307). IEEE.
15. Gashami, J. P. G., Chang, Y., Rho, J. J., & Park, M. C. (2016). Privacy concerns and benefits in SaaS adoption by individual users: A trade-off approach. Information Development, 32(4), 837-852.
16. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).
17. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
18. Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
19. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
20. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
21. Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
22. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
23. Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).
24. Gade, K. R. (2017). Integrations: ETL/ELT, Data Integration Challenges, Integration Patterns. Innovative Computer Sciences Journal, 3(1).
25. Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).
26. Muneer Ahmed Salamkar, and Karthik Allam. Architecting Data Pipelines: Best Practices for Designing Resilient, Scalable, and Efficient Data Pipelines. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
27. Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
28. Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
29. Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
30. Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
31. Naresh Dulam, et al. Data Governance and Compliance in the Age of Big Data. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Nov. 2018
32. Naresh Dulam, et al. “Kubernetes Operators: Automating Database Management in Big Data Systems”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
33. Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
34. Naresh Dulam, and Venkataramana Gosukonda. “AI in Healthcare: Big Data and Machine Learning Applications ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Aug. 2019
35. Naresh Dulam. “Real-Time Machine Learning: How Streaming Platforms Power AI Models ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
36. Sarbaree Mishra. A Distributed Training Approach to Scale Deep Learning to Massive Datasets. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
37. Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
38. Sarbaree Mishra. Distributed Data Warehouses - An Alternative Approach to Highly Performant Data Warehouses. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019
39. Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
40. Sarbaree Mishra. A Novel Weight Normalization Technique to Improve Generative Adversarial Network Training. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
41. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
42. Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
43. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
44. 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).
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