Vol. 1 No. 1 (2021): African Journal of Artificial Intelligence and Sustainable Development
Articles

Transforming Insurance Operations: Low-Code/No-Code Capabilities in Guidewire Insurance Suite

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

Published 21-01-2021

Keywords

  • Low-code,
  • Guidewire InsuranceSuite

How to Cite

[1]
Ravi Teja Madhala, “Transforming Insurance Operations: Low-Code/No-Code Capabilities in Guidewire Insurance Suite”, African J. of Artificial Int. and Sust. Dev., vol. 1, no. 1, pp. 351–372, Jan. 2021, Accessed: Dec. 28, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/236

Abstract

The insurance industry is undergoing a significant transformation, driven by the demand for agility and the rapid evolution of technology. Central to this shift is adopting low-code and no-code platforms, which allow insurers to innovate and respond to market demands with unprecedented speed and efficiency. These platforms minimize reliance on traditional IT development, enabling business users and IT teams to collaborate seamlessly in creating and refining applications. Guidewire InsuranceSuite exemplifies this evolution, offering a robust suite of tools to streamline core insurance operations, such as underwriting, claims processing, & policy administration. With intuitive drag-and-drop interfaces, prebuilt templates, and customizable workflows, insurers can rapidly prototype, test, and deploy solutions tailored to their needs, reducing development time and costs. This approach enhances operational efficiency and empowers insurers to adapt to regulatory changes, integrate emerging technologies, and offer more personalized customer experiences. By breaking free from the constraints of legacy systems, insurers can leverage Guidewire’s capabilities to create scalable and flexible systems that meet the demands of a digital-first world. Furthermore, these tools enable faster time-to-market for new products, allowing insurers to remain competitive and relevant in an increasingly dynamic environment. The ability to iterate quickly fosters innovation, as teams can experiment with new ideas without the significant risks and delays associated with traditional development methods. In addition to improving internal workflows, low-code and no-code capabilities enable insurers to elevate customer interactions by offering more responsive and customized services. This transformation helps reduce friction in claims handling and policy adjustments, delivering better customer outcomes while increasing satisfaction and loyalty. Adopting these platforms also frees technical resources to focus on high-value strategic initiatives, positioning insurers for long-term success. As the industry continues to embrace this paradigm, low-code and no-code solutions are proving to be a tool for efficiency and a catalyst for growth & innovation, reshaping the way insurance businesses operate and deliver value in an ever-evolving landscape.

Downloads

Download data is not yet available.

References

  1. Beranic, T., Rek, P., & Hericko, M. (2020, October). Adoption and usability of low-code/no-code development tools. In Central European Conference on Information and Intelligent Systems (pp. 97-103). Faculty of Organization and Informatics Varazdin.
  2. Khorram, F., Mottu, J. M., & Sunyé, G. (2020, October). Challenges & opportunities in low-code testing. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (pp. 1-10).
  3. Dunie, R., Schulte, W. R., Cantara, M., & Kerremans, M. (2015). Magic Quadrant for intelligent business process management suites. Gartner Inc.
  4. McKendrick, J. (2017). The rise of the empowered citizen developer. New Providence, NJ: Unisphere Research.
  5. Saadeldin, R. (2019). of Thesis: The fundamental analysis of the software industry in the USA. change, 2019, 29.
  6. Cope, R. (2020). Strong security starts with software development. Network Security, 2020(7), 6-9.
  7. Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
  8. Franzosa, R., & Hestermann, C. (2019). Magic quadrant for manufacturing execution systems. Gartner Inc., Stamford.
  9. Change, N. C. (2017). OF THE YEAR. Nature, 549, 431.
  10. Sarsa, H. (2017). Critical Requirements of Internal Enterprise Mobile Applications (Master's thesis).
  11. Naidoo, A. (2016). Re-engineering Modern Marketing for Organizational Growth in South Africa Post COVID. Global journal of Business and Integral Security.
  12. Baldassarre, M. T., Barletta, V. S., Caivano, D., & Scalera, M. (2020). Integrating security and privacy in software development. Software Quality Journal, 28(3), 987-1018.
  13. Taulli, T., & Taulli, T. (2020). RPA Vendors. The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems, 217-258.
  14. Woodbridge, M., Sillanpaa, M., & Severson, L. (2020). Magic Quadrant for Content Service Platforms.
  15. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*, T. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine, 151(4), 264-269.
  16. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
  17. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
  18. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).
  19. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
  20. Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
  21. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
  22. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2).
  23. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
  24. 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).
  25. Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
  26. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
  27. Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
  28. Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ Computing Journal, 1(1).
  29. Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
  30. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
  31. 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
  32. 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
  33. Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
  34. Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
  35. Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
  36. Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
  37. 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
  38. Naresh Dulam. “Real-Time Machine Learning: How Streaming Platforms Power AI Models ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
  39. Naresh Dulam, et al. “Data As a Product: How Data Mesh Is Decentralizing Data Architectures”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
  40. Naresh Dulam, et al. “Data Mesh in Practice: How Organizations Are Decentralizing Data Ownership ”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
  41. Thumburu, S. K. R. (2020). Exploring the Impact of JSON and XML on EDI Data Formats. Innovative Computer Sciences Journal, 6(1).
  42. Thumburu, S. K. R. (2020). Large Scale Migrations: Lessons Learned from EDI Projects. Journal of Innovative Technologies, 3(1).
  43. Thumburu, S. K. R. (2020). Enhancing Data Compliance in EDI Transactions. Innovative Computer Sciences Journal, 6(1).
  44. Thumburu, S. K. R. (2020). Leveraging APIs in EDI Migration Projects. MZ Computing Journal, 1(1).
  45. Thumburu, S. K. R. (2020). A Comparative Analysis of ETL Tools for Large-Scale EDI Data Integration. Journal of Innovative Technologies, 3(1).
  46. Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
  47. 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
  48. Sarbaree Mishra. “Moving Data Warehousing and Analytics to the Cloud to Improve Scalability, Performance and Cost-Efficiency”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
  49. Sarbaree Mishra, et al. “Training AI Models on Sensitive Data - the Federated Learning Approach”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
  50. Sarbaree Mishra. “Automating the Data Integration and ETL Pipelines through Machine Learning to Handle Massive Datasets in the Enterprise”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
  51. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
  52. Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
  53. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
  54. 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).