Automation and productivity with Guidewire ClaimCenter transform insurance claims.
Keywords:
Claims Transformation, IoT in Insurance, Claims Processing Integration, Claims SettlementAbstract
Insurance claims the management is quicker, easier & more efficient with Guidewires ClaimCenter. ClaimCenter automates the processes & decreases the manual duties, letting the insurers support clients during difficult times instead of focusing on the paperwork. Adjusters may be reduce mistakes & delays by managing claims publicly & the consistently. To free claims specialists to focus on a difficult situations & a customer service, ClaimCenter's dynamic platform automates the standard tasks for each claim. Its built-in data & analytics to help insurers make better judgments and spot fraud early. This speeds up claiming the handles, improves client happiness & they cuts costs. Policyholders benefit from speedier services & increased insurers confidence. Insurers may innovate & develops while maintaining the flawless operations because to the platform's flexibilities & the connection with other systems. ClaimCenter modernizes claims processing to satisfy the tech-savvy clients with speed, accuracy & empathy. It redefines claims administration, enabling insurers compete in the digital era.
References
1. Couzens, J. A. (2009, January). Implementing an enterprise system at Suncorp using Agile development. In Australian Software Engineering Conference: ASWEC (Vol. 20, pp. 35-39).
2. Seddon, P. B., Reynolds, P., & Willcocks, L. P. (2010). Post-merger IT integration: a comparison of two case studies.
3. Gregory, M. (2016). Big Data, Big Decisions. Journal of the Australian & New Zealand Institute of Insurance & Finance, 39(2).
4. Baecke, P., & Bocca, L. (2017). The value of vehicle telematics data in insurance risk selection processes. Decision Support Systems, 98, 69-79.
5. Hopkins, J., & Hawking, P. (2018). Big Data Analytics and IoT in logistics: a case study. The International Journal of Logistics Management, 29(2), 575-591.
6. Saheb, T., & Izadi, L. (2019). Paradigm of IoT big data analytics in the healthcare industry: A review of scientific literature and mapping of research trends. Telematics and informatics, 41, 70-85.
7. Metsola, J. (2019). Bringing Valuable Data to Transportation Companies with Advanced Vehicle Telematics.
8. Sărăcin, A., Coșarcă, C., Savu, A., Negrilă, A. F. C., & Didulescu, C. (2018). Telematics and intelligent transport. International Multidisciplinary Scientific GeoConference: SGEM, 18(2.2), 427-434.
9. Manso, M., Guerra, B., Carjan, C., Sdongos, E., Bolovinou, A., Amditis, A., & Donaldson, D. (2018). The application of telematics and smart devices in emergencies. Integration, Interconnection, and Interoperability of IoT Systems, 169-197.
10. Bujak, A. (2018). The development of telematics in the context of the concepts of “Industry 4.0” and “Logistics 4.0”. In Management Perspective for Transport Telematics: 18th International Conference on Transport System Telematics, TST 2018, Krakow, Poland, March 20-23, 2018, Selected Papers 18 (pp. 509-524). Springer International Publishing.
11. 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.
12. Luboz, V., Zhai, J., Odetoyinbo, T., Littler, P., Gould, D., How, T., & Bello, F. (2011). Guidewire and catheter behavioural simulation. Medicine Meets Virtual Reality 18, 317-323.
13. Von Jako, R. A., Carrino, J. A., Yonemura, K. S., Noda, G. A., Zhue, W., Blaskiewicz, D., ... & Weber, G. (2009). Electromagnetic navigation for percutaneous guide-wire insertion: accuracy and efficiency compared to conventional fluoroscopic guidance. Neuroimage, 47, T127-T132.
14. 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.
15. Zhou, X. H., Bian, G. B., Xie, X. L., & Hou, Z. G. (2018). An interventionalist-behavior-based data fusion framework for guidewire tracking in percutaneous coronary intervention. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(11), 4836-4849.
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. Apache Spark: The Future Beyond MapReduce. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Dec. 2015, pp. 136-5
32. Naresh Dulam. NoSQL Vs SQL: Which Database Type Is Right for Big Data?. Distributed Learning and Broad Applications in Scientific Research, vol. 1, May 2015, pp. 115-3
33. Naresh Dulam. Data Lakes: Building Flexible Architectures for Big Data Storage. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Oct. 2015, pp. 95-114
34. Naresh Dulam. The Rise of Kubernetes: Managing Containers in Distributed Systems. Distributed Learning and Broad Applications in Scientific Research, vol. 1, July 2015, pp. 73-94
35. Naresh Dulam. Snowflake: A New Era of Cloud Data Warehousing. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Apr. 2015, pp. 49-72
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).
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
