AI-Powered Process Mining for Intelligent, Personalized Customer Experience in the Insurance Sector
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Abstract
The current paper will describe the AI-based system of process mining that is specifically oriented to redesigning the customer experience in the insurance industry, relying on the high-quality data supply, the workflow optimization based on the AI model, and the real-time personalization. The combination of the three fundamental layers is its methodology and contains (1) Data Quality & Governance, (2) AI-driven Process Intelligence, and (3) Personalized Decision Support. To attain consistent, trusted, unified datasets, the solid data quality approach is exercised on automated metadata capture, master data management, anomaly recognition, and real-time data validation pipelines and assures coherent and trusted datasets in policy systems, claims, and customer interaction systems. It is an organized and clean database that will provide reconstruction of the customer journey and high-quality process models.
The process mining methods (AI) are then followed by the first part of the process, which is necessitated by the analysis of the end-to-end workflows, and then extract event logs, sequence modelling, and the conformance checking. The machine learning models identify the existence of bottlenecks, foretell delays, and will and patterns that impact the complexity of the churn or claim. On generative AI, operationally generated insights, draft customer messages, and simulated what-if underwriting decisions and claims are created.
Its impacts have already found their way into real-time applications: insurers have already shortened by 22-30% a claims cycle time, automated by 40-60% routine customer contacts with intent-conscious virtual agents, and improved fraud detection by 18-25% with beam-of-thought anomaly models. Individualized policy recommendations regarding customer life stage analytics, sentiment data, and situational indicators have resulted in a boost of responses and cross-sell conversion of more than 15%.
Findings have revealed that with the convergence of process mining, predictive analytics, and customer-specific generative AI, the insurers would be able to provide more rapid, transparent, and personalized experiences at scale and portray a significant behavioral change where the operations of serving a client are reactive as compared to anticipatory and, as a result, customer-driven.
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