Impact of data warehousing adoption on underwriting and claims performance in Saudi insurance firms
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DOIhttp://dx.doi.org/10.21511/ins.17(1).2026.05
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Article InfoVolume 17 2026, Issue #1, pp. 65-77
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Type of the article: Research Article
Abstract
Saudi insurers increasingly face demands to analyze huge quantities of underwriting and claims data, but disparate systems may decrease the precision of pricing and the speed of claim settlement, thereby reducing operational efficiency. The purpose of this study is to identify if data warehousing adoption could contribute positively to the underwriting and claims handling operations of insurance companies in Saudi Arabia. The study employed fixed-effect regressions to examine the relationship between data warehousing adoption and underwriting/claims handling operations of insurance companies based on a firm-year fixed-effect panel data set of 2015–2024, including information technology investment intensity interaction terms. The result of this study indicated that data warehousing adoption is positively related to underwriting/claims handling operations of insurance companies, where data warehousing adoption could contribute positively to reducing loss ratio by 4.8 percent (β = –0.048, P < 0.01) and combined ratio by 5.6 percent (β = –0.056, P < 0.01). Data warehousing adoption could also contribute positively to claims handling operations, where average claim settlement time could be reduced by 6.21 days (β = –6.21, P < 0.05). In addition, the data warehousing investment interaction term can provide an additional 3.2 percentage points of improvement (β = –0.032, p < 0.05), implying that data warehousing value can be enhanced by complementary investments in information technology capabilities. Explanatory powers of the model are considerable, with R-squared of 0.41-0.52 for different equations.
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JEL Classification (Paper profile tab)G22, M15, C23, O33
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References56
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Tables7
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Figures0
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- Table 1. Variable definitions and measurements
- Table 2. Descriptive statistics of variables
- Table 3. Pearson correlation matrix of key variables
- Table 4. Regression results (fixed effects model)
- Table 5. Moderating effect of IT investment
- Table 6. Summary of hypotheses testing results
- Table A1. Sample insurance and reinsurance firms (n = 25)
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