Real-Time Streaming AI in Claims Adjudication for High-Volume TPA Workloads
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P105Keywords:
Real-time streaming, AI in claims adjudication, TPA workloads, Apache Kafka, Spark Streaming, machine learning inference, claims processing, in-flight claim scoring, data streaming architecture, predictive analytics, decision-making systems, high-volume claims, automated claim adjudication, data pipeline, cloud-based infrastructure, real-time data processing, fraud detection, risk assessment, microservices architecture, scalability, data security, privacy compliance, event-driven architectureAbstract
Actual time AI-driven claims adjudication greatly improves more efficiency & also decision-making in high- volume Third-Party Administrator (TPA) operations. Using Apache Kafka, Spark Streaming, and ML inference pipelines among many other current technologies this system generates seamless, actual time claim scoring and processing. While Spark Streaming provides strong actual time analytics, providing low-latency processing of their significant volumes of claims data, Kafka is a more consistent data streaming platform that deftly manages the relentless flow of claims information. By use of established models, automated decision-making is facilitated by ML inference pipelines, therefore offering more predictive insights for their risk management, fraud detection & also claims more validation. These technologies help to reduce more human error, increase operational efficiency & hasten claim processing timelines. The actual time processing powers provide fast adjudication of claims, therefore improving more customer satisfaction and reducing third-party administrator expenses. Emphasizing its practical benefits including faster claims processing, improved accuracy & more scalability to meet growing workload needs the article offers a case study that shows the efficient implementation of this system in a high-volume environment. This latest approach shows that the combination of actual time streaming and AI might improve claims adjudication, therefore improving TPA operations over time and increasing their efficiency, automated, intelligent nature
References
[1] Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.
[2] Ahuja, Ashutosh, and Predictive Analytics. "REVOLUTIONIZING CLAIM ADJUDICATION: DESIGNING INTELLIGENT, PANDEMIC-RESILIENT CONTACT CENTER SYSTEMS IN HEALTHCARE TECHNOLOGY." Well Testing Journal 31.1 (2022): 64-83.
[3] Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “AI-Powered Workflow Automation in Salesforce: How Machine Learning Optimizes Internal Business Processes and Reduces Manual Effort”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 3, Apr. 2023, pp. 149-71
[4] Ebers, Martin. "Automating due process-the promise and challenges of AI-based techniques in consumer online dispute resolution." Frontiers in Civil Justice. Edward Elgar Publishing, 2022. 142-168.
[5] Schmitz, Amy J. "Expanding access to remedies through e-court initiatives." Buff. L. Rev. 67 (2019): 89.
[6] Atluri, Anusha. “Post-Deployment Excellence: Advanced Strategies for Agile Oracle HCM Configurations”. International Journal of Emerging Research in Engineering and Technology, vol. 4, no. 1, Mar. 2023, pp. 37-44
[7] Cashman, Peter, and Eliza Ginnivan. "Digital justice: Online resolution of minor civil disputes and the use of digital technology in complex litigation and class actions." Macquarie LJ 19 (2019): 39.
[8] Yasodhara Varma. “Scalability and Performance Optimization in ML Training Pipelines”. American Journal of Autonomous Systems and Robotics Engineering, vol. 3, July 2023, pp. 116-43
[9] Engstrom, David Freeman, and Jonah B. Gelbach. "Legal tech, civil procedure, and the future of adversarialism." University of Pennsylvania Law Review (2021): 1001-1099.
[10] 1Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “Voice AI in Salesforce CRM: The Impact of Speech Recognition and NLP in Customer Interaction Within Salesforce’s Voice Cloud”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 3, Aug. 2023, pp. 264-82
[11] Sourdin, Tania, and John Zeleznikow. "Courts, mediation and COVID-19." Australian Business Law Review 48.2 (2020): 138-158.
[12] Syed, Ali Asghar Mehdi. “Networking Automation With Ansible and AI: How Automation Can Enhance Network Security and Efficiency”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 3, Apr. 2023, pp. 286-0
[13] Varma, Yasodhara, and Manivannan Kothandaraman. “Optimizing Large-Scale ML Training Using Cloud-Based Distributed Computing”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 3, no. 3, Oct. 2022, pp. 45-54
[14] Koh, Harold Hongju. "The" Gants Principles" for Online Dispute Resolution: Realizing the Chief Justice's Vision for Courts in the Cloud." BCL Rev. 62 (2021): 2768.
[15] Kupunarapu, Sujith Kumar. "AI-Driven Crew Scheduling and Workforce Management for Improved Railroad Efficiency." International Journal of Science And Engineering 8.3 (2022): 30-37.
[16] Wing, Leah, et al. "Designing ethical online dispute resolution systems: the rise of the fourth party." Negotiation Journal 37.1 (2021): 49-64.
[17] Varma, Yasodhara. “Scaling AI: Best Practices in Designing On-Premise & Cloud Infrastructure for Machine Learning”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 2, June 2023, pp. 40-51
[18] Subramanyam, Sasikiran Vepanambattu. "AI-powered process automation: Unlocking cost efficiency and operational excellence in healthcare systems." International Journal of Advanced Research in Engineering and Technology (IJARET) 13.1 (2022): 86-102.
[19] Kupunarapu, Sujith Kumar. "AI-Enhanced Rail Network Optimization: Dynamic Route Planning and Traffic Flow Management." International Journal of Science And Engineering 7.3 (2021): 87-95.
[20] Atluri, Anusha. “Redefining HR Automation: Oracle HCM’s Impact on Workforce Efficiency and Productivity”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, June 2021, pp. 443-6
[21] Dal Pubel, Luca. "The role of information and communication technology and the Internet in the acces to justice: effective online dispute resolution in low-value, high-volume business-to-consumer (B2C) cross-border e-Commerce disputes." (2014).
[22] Chaganti, Krishna C. "Leveraging Generative AI for Proactive Threat Intelligence: Opportunities and Risks." Authorea Preprints.
[23] Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare: promise and potential." Health information science and systems 2 (2014): 1-10.
[24] Vasanta Kumar Tarra. “Policyholder Retention and Churn Prediction”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 10, no. 1, May 2022, pp. 89-103
[25] Avery, Taliser R., et al. "Near real‐time adverse drug reaction surveillance within population‐based health networks: methodology considerations for data accrual." Pharmacoepidemiology and drug safety 22.5 (2013): 488-495.
[26] Syed, Ali Asghar Mehdi. “Edge Computing in Virtualized Environments: Integrating Virtualization and Edge Computing for Real-Time Data Processing”. Essex Journal of AI Ethics and Responsible Innovation, vol. 2, June 2022, pp. 340-63
[27] Anand, Sangeeta. “AI-Based Predictive Analytics for Identifying Fraudulent Health Insurance Claims”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 2, June 2023, pp. 39-47
[28] Kanchetti, Devidas. "Optimization of insurance claims management processes through the integration of predictive modeling and robotic process automation." International Journal of Computer Applications (IJCA) 2.2 (2021): 1-18.
[29] Atluri, Anusha, and Vijay Reddy. “Total Rewards Transformation: Exploring Oracle HCM’s Next-Level Compensation Modules”. International Journal of Emerging Research in Engineering and Technology, vol. 4, no. 1, Mar. 2023, pp. 45-53
[30] Biehl, João, et al. "Judicialization 2.0: Understanding right-to-health litigation in real time." Global public health 14.2 (2019): 190-199.
