Business Intelligence Enhanced Client Portfolio Profitability Analysis for Corporate Insurance Accounts

Abstract

Corporate insurance portfolio decisions often depend on profitability reports assembled from fragmented policy, claims, and finance systems, which weakens confidence in results and delays corrective action across pricing, renewals, and loss-control activities. This study examined how business intelligence capability strengthens portfolio profitability analysis effectiveness and whether governance and execution mechanisms further enhance that effect within enterprise cloud-enabled reporting environments. The research adopted a quantitative cross-sectional, case-based design using a five-point Likert survey administered across corporate insurance portfolio operations. Purposive sampling yielded 162 usable responses with at least 95 percent completeness, covering underwriting at 34.6 percent, claims at 23.5 percent, finance at 18.5 percent, portfolio management at 14.2 percent, and BI roles at 9.3 percent, while 71.0 percent of respondents reported weekly or daily dashboard use. Key variables included BI capability comprising data quality, integration and accessibility, reporting and dashboarding, and analytics capability, as well as KPI consistency, portfolio segmentation usefulness, insight-to-action execution, and portfolio profitability analysis effectiveness. The analysis plan applied descriptive statistics to summarize construct levels, Cronbach’s alpha to test reliability, Pearson correlations to assess associations, and multiple regression to estimate the unique contribution of predictors to profitability analysis effectiveness. BI capability was moderate to high with a mean of 3.84 and standard deviation of 0.63, analytics capability was highest with a mean of 4.00 and standard deviation of 0.61, and integration and accessibility was lowest with a mean of 3.69 and standard deviation of 0.74, while profitability analysis effectiveness averaged 3.88 with a standard deviation of 0.65. Reliability was strong with BI capability alpha at .91 and effectiveness alpha at .88. BI capability showed a strong positive correlation with effectiveness at r = .62 and p < .001, and similarly strong relationships were observed for KPI consistency at r = .55 and p < .001 and insight-to-action at r = .60 and p < .001. The regression model explained 54 percent of variance in effectiveness with R² = .54 and F(4,157) = 46.21 and p < .001, with significant effects for BI capability at β = .34 and p < .001, KPI consistency at β = .21 and p = .001, segmentation usefulness at β = .14 and p = .026, and insight-to-action at β = .29 and p < .001. These findings imply that firms should pair BI investments with KPI governance and disciplined action routines, prioritize integration and a shared semantic layer, and embed BI outputs into portfolio decision workflows to convert insights into consistent profitability improvements.