AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V2I1P107Keywords:
Guidewire, Artificial Intelligence, API Integration, Real-time Data, Digital Insurance, Machine LearningAbstract
Artificial intelligence (AI) and Application Programming Interfaces (APIs) are two aspects of revolutionizing the fast-changing world of digital insurance. The number of data consumption and smart automation continues to rise as the insurance platform Guidewire upgrades its services to offer agility and operational performance. This paper outlines the opportunities to architect AI-augmented API integrations in the Guidewire ecosystem, with a particular focus on real-time data flow, analytics, and decision-making. The article presents an elaborate framework for implementing AI-driven APIs, including the integration of cloud data lakes, stream processing engines, and machine learning modules. Additionally, it pertains to the development of the API application in insurance, specifically addressing the critical issues of real-time processing and how AI addresses them through anomaly detection and self-healing integration flows. Implementation outcomes are also assessed in the paper in terms of applications, including claims automation, underwriting and fraud detection. The outcomes indicate that the process latency decreased significantly (by up to 45%) and that accuracy regarding the data improved by 60%. Lastly, we explain how this paradigm shift in the API ecosystem towards an AI-native paradigm redefines the value delivery models of insurance
References
[1] Tien, J. M. (2017). Internet of Things, real-time decision-making, and artificial intelligence. Annals of Data Science, 4(2), 149-178.
[2] Collins, G. C., Sarma, A., Bercu, Z. L., Desai, J. P., & Lindsey, B. D. (2020). A robotically steerable guidewire with forward-viewing ultrasound: Development of technology for minimally invasive imaging. IEEE Transactions on Biomedical Engineering, 68(7), 2222-2232.
[3] Andročec, D. (2015). Application Programming Interfaces (APIs) Based Interoperability of Cloud Computing (Doctoral dissertation, University of Zagreb, Faculty of Organization and Informatics, Varaždin).
[4] Schatten, M., Ðurić, B. O., & Tomičić, I. (2018). Towards an application programming interface for automated testing of artificial intelligence agents in massively multiplayer online role-playing games. In Central European Conference on Information and Intelligent Systems (pp. 11-15). Faculty of Organization and Informatics Varazdin.
[5] He, D., Wang, Z., & Liu, J. (2018). A survey to predict the trend of AI-able server evolution in the cloud. IEEE Access, 6, 10591-10602.
[6] Mackenzie, A. (2019). From API to AI: Platforms and Their Opacities. Information, Communication & Society, 22(13), 1989-2006.
[7] Soni, M. (2015, November). End-to-end automation on cloud with build pipeline: the case for DevOps in the insurance industry, continuous integration, continuous testing, and continuous delivery. In 2015, the IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) (pp. 85-89). IEEE.
[8] Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, delivery and deployment: a systematic review on approaches, tools, challenges and practices. IEEE Access, 5, 3909-3943.
[9] Claps, G. G., Svensson, R. B., & Aurum, A. (2015). On the journey to continuous deployment: Technical and social challenges along the way. Information and Software Technology, 57, 21-31.
[10] Gadge, S., & Kotwani, V. (2018). Microservice architecture: API gateway considerations. GlobalLogic Organisations, Aug-2017, 11.
[11] Ketterer, H., Koopmans, J., & Mäurers, R. (2016). Building a Digital Technology Foundation in Insurance. The Boston.
[12] Narkhede, N., Shapira, G., & Palino, T. (2017). Kafka: the definitive guide: real-time data and stream processing at scale. "O'Reilly Media, Inc.".
[13] Bonissone, P. P. (2015). Machine learning applications. In Springer Handbook of Computational Intelligence (pp. 783-821). Berlin, Heidelberg: Springer Berlin Heidelberg.
[14] Bello, O., Yang, D., Lazarus, S., Wang, X. S., & Denney, T. (2017, May). Next-generation downhole big data platform for dynamic, data-driven well and reservoir management. In SPE Reservoir Characterisation and Simulation Conference and Exhibition (p. D031S014R002). SPE.
[15] Ferguson, M. (2012). Architecting a big data platform for analytics. A Whitepaper prepared for IBM, 30.
[16] Zheng, T., Chen, G., Wang, X., Chen, C., Wang, X., & Luo, S. (2019). Real-time intelligent big data processing: technology, platform, and applications. Science China Information Sciences, 62(8), 82101.
[17] Wingerath, W., Gessert, F., Friedrich, S., & Ritter, N. (2016). Real-time stream processing for Big Data. it-Information Technology, 58(4), 186-194.
[18] Shahin, M., Babar, M. A., Zahedi, M., & Zhu, L. (2017, November). Beyond continuous delivery: an empirical investigation of continuous deployment challenges. In 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (pp. 111-120). IEEE.
[19] Yasumoto, K., Yamaguchi, H., & Shigeno, H. (2016). Survey of real-time processing technologies of IoT data streams. Journal of Information Processing, 24(2), 195-202.
[20] Tantalaki, N., Souravlas, S., & Roumeliotis, M. (2020). A review of big data real-time stream processing and its scheduling techniques. International Journal of Parallel, Emergent and Distributed Systems, 35(5), 571-601.
[21] Pappula, K. K. (2020). Browser-Based Parametric Modeling: Bridging Web Technologies with CAD Kernels. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 56-67. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P107
[22] Enjam, G. R., & Chandragowda, S. C. (2020). Role-Based Access and Encryption in Multi-Tenant Insurance Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 1(4), 58-66. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I4P107
