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Technology investments and firm performance under the wave of InsurTech

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Abstract

This study explores how the technology investments of a sample of U.S. property-liability insurers affected their performance during the InsurTech wave. The critical question is whether insurers' InsurTech-oriented investments strengthen their competitive advantages, resulting in improved firm performance. This study reveals that insurers' InsurTech-oriented investments have a significant detrimental influence on their short-term performance. Intriguingly, empirical findings indicate that insurers' InsurTech-oriented investments have a strong positive relationship with long-term performance, suggesting that the notion of time lag and cumulative impacts is justified. The study provides new insight into the effect insurers' InsurTech-oriented investments have on their performance.

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Notes

  1. I thank the anonymous reviewer who proposed that insurers' investments in InsurTech architecture through acquisitions or joint ventures will not appear as electronic data processing equipment and software (EDP) expenses. Regarding joint ventures and M&A, these investments are reflected in the company's share but not in the EDP item. Based on the author’s unreported survey, among the top 20 insurers that have invested in EDP many are also engaged in ventures or M&A. It is evident that these investments are share investments that do not appear as EDP investments or expenses. The author concurs that the EDP proxy in this paper has limitations, primarily due to the substantial assets of measured technology in software, hardware, and infrastructure. The issue of share investments (such as joint ventures, M&A, and strategic alliances) cannot be discussed currently, but the topic is worthy of future discussion.

  2. These values are derived from the samples utilised for this study.

  3. According to the NAIC Underwriting and Investment Exhibit, Part 3—Expenses, page 11, 2018), EDP expenses are primarily comprised of the cost and depreciation of the EDP equipment and software, which may be related to the firm's InsurTech-oriented investments, especially the technological investments in the underwriting and claim process. The replacement of hardware and software is primarily intended to increase the effectiveness of the underwriting and claim process, marketing, human resource management (HRM), and customer relationship management (CRM), as well as to improve administrative efficiency and reduce costs.

  4. Based on the NAIC declaration information rule of SSAP # 16R—Electronic Data Processing Equipment and Software (https://content.naic.org/sites/default/files/inline-files/016_l.pdf), the depreciated value of all capitalised EDP equipment and software is reported on the NAIC Assets sheet (Page 2; the 2018). According to SSAP #16R, the EDP input consists primarily of the operating system software, the EDP equipment, and other software. The, operating system software (e.g. DOS and Windows) comprises all software required to operate and maintain hardware. Examples of EDP equipment are mini-computers, personal computers, laptops, and peripheral equipment such as disc drivers, tape drives, and printers. The depreciation period for the EDP equipment and operating system software should not exceed three years. Lastly, non-operating system software (often called "applications software") consists of applications such as the general ledger, policy administration, investment management, claims to process, and premium billing.

  5. I appreciate the reviewer's suggestion on confirming the relevance of EDP and insurers' overall InsurTech-oriented investments. In response to the reviewer's recommendation, the author surveyed leading technology investment firms (based on EDP expenses in the NAIC statements) to determine if they are also the firms that consulting firms and others identify as the leading InsurTech firms (based on insurers' overall InsurTech-oriented investments). Overall, based on the untabulated survey results, there is strong correlation between an insurance company's EDP expenses and its overall InsurTech-related engagements and investments.

  6. Based on the prior discussion, it can be inferred from the examples of GEICO and HFIC that insurers may invest in EDP, such as e-commerce, internet technologies, IoT, social networking services, social media, AI, and BDA. The primary objective is for insurers to increase the efficiency of underwriting and claims processing, marketing, HRM, and CRM, as well as to reduce administrative costs and improve organizational efficiency. Even though there is no direct evidence to show that the company's investments in EDP are related to e-commerce, internet technology, IoT, social networking services, social media, AI, and BDA, the cases of GEICO and HFIC provide strong evidence to show that EDP' investments are related to FinTech- or InsurTech-oriented investments.

  7. The two-Stage regression analysis includes the two-stage general moment method (two-stage GMM), the general two-stage least squares (G2SLS), and the Heckman two-stage regression model.

  8. I thank the anonymous reviewer for suggesting a robustness test for the lag EDP measure. Two-stage regression baseline models have been reevaluated using EDPt−1. Overall, the empirical results of employing EDPt−1 are consistent with the main findings of this paper. In an effort to simplify the article, the results are not tabulated in this paper.

  9. In order to mitigate the potential endogenous problem posed by the control variables, this paper lags all control variables, as suggested by the literature (e.g. Elango et al. 2008).

  10. I thank the anonymous reviewer for suggesting that the issue of selecting instrumental variables (IVs) be addressed. This study employs the Durbin–Wu–Hausman endogeneity test. Based on the prior literature (e.g. Majumdar 1995; Dasgupta et al. 1999; Giunta and Trivieri 2007; DeYoung et al. 2007; Neale et al. 2020; Che et al. 2021), several exogenous variables, such as loss ratio, firm leverage, profitability volatility, firm age, market share, and the proportion of premiums issued in auto lines, homeowners multiple peril lines, and worker's compensation lines, could be treated as IVs. However, some exogenous variables may not satisfy the exogeneity requirements. Based on extensive tests, including the Durbin–Wu–Hausman endogeneity test (i.e., Hausman specification test), the under-identification test (Kleibergen–Paap rank LM statistics), the weak identification test (Cragg–Donald Wald F statistic), and the overidentification test (Hansen J statistics), the author concludes that firm leverage, profitability volatility, and firm age meet these necessary conditions. It suggests that these three exogenous variables could mitigate the endogeneity problem in this paper.

  11. This paper uses the fundamental concepts of Kyock (1954) and Mirzaei et al. (2016) to evaluate the long-term cumulative impact of technology investments. These two papers explain that advertising expenditures will have a cumulative effect over time. Consequently, this study hypothesises that insurers' technology investments also exhibit a cumulative effect over time. In general, a company may invest in technology sequentially; therefore, the cumulative impact of each year's investments in technology can take years to manifest. In addition, according to the argument of Campbell (2012), this paper hypothesizes that specific cumulative benefits of technology investments may manifest after 3–5 years.

  12. I thank the anonymous reviewer who suggested that sensitivity analysis for different parameters of r is critical. This study re-tests the empirical results by re-setting the parameter r to different values (r = 1.2, 1.3, and 1.4). Overall, the empirical results demonstrate that the results with different parameter settings resemble the main results of this paper, which are robustly consistent with the main findings in this paper. These results are not tabulated here.

  13. I thank the anonymous reviewer who proposed that using ROA and ROC to explain the impact of cumulative technology on firm performance is economically relevant (Liebenberg and Sommer 2008). ROA and ROC are alternative firm performance indicators defined as net income after tax divided by admitted assets and surplus, respectively (e.g. Pottier and Sommer 1999, Lai and Limpaphayom 2003; Wang et al. 2007; Elango et al. 2008; Ma and Elango 2008; Pope and Ma 2008). However, the models of ROA and ROC measures failed the under-identification test (Kleibergen–Paap rank LM statistics) and the weak identification test (Cragg–Donald Wald F statistic) when detecting endogenous problems. Hence, in this paper, all regression results are presented concerning RAROA and RAROC measures rather than ROA and ROC. These results are not tabulated here.

  14. The author sorted EDP expenses from 2015 to 2019 by summing EDP expenses and then identified the top 20 EDP expense insurers. Information about insurers' InsurTech-oriented investments was researched extensively via Google, insurers' official websites, and other online media, as well as industry research reports of Towers Watson & Co (i.e., Willis Towers Watson). The survey period is from 2015 to 2022, primarily because the investment and utilization of InsurTech by each firm over the past two years must be reported to emphasis the EDP metric associated with InsurTech-oriented investments. In addition, a few insurers are unable to locate information regarding InsurTech-oriented investments from a prior period. Consequently, recent InsurTech-oriented investments information is supplied. Due to the issue of commercial conflict of interest, the survey results of the top 20 EDP expense insurers that engage in InsurTech-oriented investments are not tabulated in this study. If interested, readers can request via email.

  15. I thank the anonymous reviewer who suggested it is critical to distinguish between insurers with and without EDP. Firstly, this study implements the mean difference test of EDP measures for insurers with and without EDP, and the testing results indicate that the asset- and expense-EDP measurements differ significantly. Secondly, this study further conducts relevant analyses for the top 20 insurers that have invested in EDP over the past five years (2015–2019), including a mean difference test and a two-stage regression analysis. Using the method of two way sorting (e.g. size and commercial line ratio), the author selects three matching firms for each of the top 20 EDP insurers. The author employs two identical firm-sample matrices. The first is matching firm with EDP = 0, and the second is that the EDP is not necessarily equal to 0. The testing results indicate that the EDP_asset of the top 20 EDP firms is significantly greater than firms with EDP = 0. Moreover, even though the mean difference is positive, the results reveal that the performance of the top 20 insurers invested in EDP is insignificantly superior to that of their matching firms. Thirdly, the author conducts Hackman's two-stage analyses for these two different firm-sample matching matrices. The empirical results indicate that the performance of the top 20 insurers invested in EDP is insignificantly superior to control firms for matching firms with EDP = 0, whereas an intriguingly positive relationship between these two groups for matching firms with the EDP is not necessarily equal to 0.

  16. I thank the anonymous reviewer who suggested this critical question.

  17. Data source: Capital IQ COMPUSTAT dataset.

  18. Market-to-book ratio is a widely used measure of the present value of expected future abnormal profits (Lev and Sougiannis 1999).

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Acknowledgements

The author is grateful for the funding provided by the Ministry of Science and Technology, Taiwan [Grant Number MOST 107-2410-H-324-001].

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  1. Department of Risk Management and Insurance, Feng Chia University, No. 100, Wenhwa Rd., Seatwen, Taichung, 407102, Taiwan

    Vincent Y. L. Chang

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Chang, V.Y.L. Technology investments and firm performance under the wave of InsurTech. Geneva Pap Risk Insur Issues Pract (2023). https://doi.org/10.1057/s41288-023-00286-w

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