Data-Driven Framework for Service Issue Escalation and Resolution in Large Scale Insurance Portfolios

Abstract

Service issue escalation and resolution in cloud enabled enterprise case management are difficult to govern in large insurance portfolios, where tickets can trigger avoidable escalation, stalled ownership, and inconsistent closure. This study developed and tested a data driven framework linking escalation criteria standardization (ECS), workflow automation support (WAS), cross functional coordination (CFC), data quality (DQ), analytics effectiveness (AE), and governance and accountability (GOV) to escalation effectiveness (EE) and resolution performance (RP). Using a quantitative, cross sectional, case-based design, a five-point Likert survey captured perceptions from 228 employees in one enterprise portfolio using a cloud-based case management workflow (frontline 42.1%, specialists 28.5%, supervisors 19.3%, QA or support 10.1%). Analysis included data screening, Cronbach reliability, descriptive statistics, Pearson correlation, two multiple regression models, and a mediation test. Internal consistency was high (alpha 0.83 to 0.91). Mean scores indicated moderate capability but uneven execution (3.42 to 4.11), and pathway integrity was weakest on documentation completeness (M 3.41) and ownership continuity (M 3.38) compared with routing accuracy (M 3.79). Correlations supported key links, including ECS with EE (r 0.56), WAS with EE (r 0.49), CFC with EE (r 0.52), DQ with AE (r 0.58), AE with RP (r 0.61), EE with RP (r 0.57), and GOV with RP (r 0.46), all with p below 0.001. Regression results showed ECS (beta 0.31), WAS (beta 0.24), and CFC (beta 0.29) explained 54% of variance in EE (R2 0.54), while AE (beta 0.33), EE (beta 0.28), GOV (beta 0.19), and DQ (beta 0.17) explained 62% of RP (R2 0.62). EE partially mediated ECS to RP, with the direct effect decreasing from beta 0.34 to 0.21 and an indirect effect of 0.13 (p below 0.01). These findings highlight actionable levers for service leaders and BI governance. Implications are that teams can improve closure speed and durability by enforcing complete handoff packages, expanding automated routing and aging controls, and using analytics dashboards under clear ownership rules.