Dataset of an actual life-risk insurance portfolio

Lledó, Josep; Pavía, José M.

doi:10.1016/j.dib.2022.108655

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…The total size of the portfolio, including both sexes, contains 76,102 policies, being the third season (summer) the quarter with less sales. The data set and more details about it are available in Lledó and Pavía (2022).…”

Section: Empirical Results With Null Loadingsmentioning

confidence: 99%

“…Before analysing the numbers in Tables 1 and 2, we should note that some of the differences are not a consequence of the pricing strategies, but a consequence of differences in (i) capitals at risk among the policies, (ii) the particular age distribution of the portfolio, (iii) the uneven impacts of rounding when calculating the actuarial ages, and (iv) the unequal distribution of sales among quarters. Indeed, in the portfolio considered, almost a third of the policies are renewed during the last quarter of the calendar year, almost two-thirds of the insured are men and the majority of policyholders fall between the ages of 40 to 60 (Lledó and Pavía, 2022). Thus, to add robustness to the conclusions, we also study how points (i)–(iv) impact on the results after controlling for their variability, that is by keeping them constant.…”

Section: Empirical Results With Null Loadingsmentioning

confidence: 99%

“…In order to assess the different shortcuts in our competitive market, in the next two sections, we compute the expected values of the technical profits, , that Jahr, Viertel and Ungefahr would gain competing in a hypothetical market of buyers made up of the insured of the actual portfolio for the year 2009 of a Spanish life insurance company (Lledó and Pavía, 2022). We calculate their market shares and their (relative) expected profits in 12 different scenarios.…”

Section: Assessing the Impact Of The Shortcuts In A Competitive Marketmentioning

confidence: 99%

“…Emigrant and immigrant microdata come from the Statistics of Residential Variation and are available in the link: <https://goo.su/rjnoja>. The portfolio of life insurance policies is available in Lledó and Pavía (2022).…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

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Shortcuts for the construction of sub-annual life tables

Pavía

Lledó

2023

ASTIN Bull.

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Fuelled by the big data explosion, a new methodology to estimate sub-annual death probabilities has recently been proposed, opening new insurance business opportunities. This new approach exploits all the detailed information available from millions of microdata records to develop seasonal-ageing indexes (SAIs) from which sub-annual (quarterly) life tables can be derived from annual tables. In this paper, we explore whether a shortcut could be taken in the estimation of SAIs and (life insurance) sub-annual death rates. We propose three different approximations, in which estimates are attained by using just a small bunch of thousands of data records and assess their impact on several competitive markets defined from an actual portfolio of life insurance policies. Our analyses clearly point to the shortcuts as good practical alternatives that can be used in real-life insurance markets. Noticeably, we see that embracing the new quarterly based approach, even using only an approximation (shortcut), is economically preferable to using the associated annual table, offering a significant competitive advantage to the company adopting this innovation.

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Section: Empirical Results With Null Loadingsmentioning

confidence: 99%

Section: Empirical Results With Null Loadingsmentioning

confidence: 99%

Section: Assessing the Impact Of The Shortcuts In A Competitive Marketmentioning

confidence: 99%

Section: Availability Of Data and Materialsmentioning

confidence: 99%

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Shortcuts for the construction of sub-annual life tables

Pavía

Lledó

2023

ASTIN Bull.

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“…The availability of open access data concerning insured populations is currently limited. In life insurance, a vast database containing valuable information can be accessed at [2] concerning ages, risk capital and quarterly values. In the realm of non-life insurance, there are existing databases that pertain to particular areas, car insurance, property insurance, and health insurance.…”

Section: Exploring the Insurance Industrymentioning

confidence: 99%

Dataset of an actual motor vehicle insurance portfolio

Segura-Gisbert,

Lledó,

Pavía

2023

Preprint

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Advanced analytics plays a vital role in enhancing various aspects of business operations within the insurance sector, providing valuable insights that drive informed decision-making, primarily through effective database utilization. However, open access databases in the insurance industry are exceedingly rare, as they are the basis of the business, encapsulating all the risk structure of the company. This makes it challenging for researchers and practitioners to access comprehensive insurance datasets for analysis. This paper introduces an extensive database specifically tailored for non-life motor insurance, containing 105,555 rows and encompassing a wide array of 30 variables. The dataset comprises important date-related information, such as effective date, date of birth of the insured, and renewal date, essential for policy management and risk assessment. Additionally, it includes relevant economic variables, such as premiums and claim costs, for assessments of products’ financial profitability. Moreover, the database features an array of risk-related variables, such as vehicle size, economic value, power, and weight, which significantly contribute to understanding risk dynamics. By leveraging the statistical analysis of this rich database, researchers could identify novel risk profiles, reveal variables that influence insured claims behaviour, and contribute to the advancement of educational and research initiatives in the dynamic fields of economics and actuarial sciences. The availability of this comprehensive database opens new opportunities for research and teaching and empowers insurance professionals to enhance their risk assessment and decision-making processes.

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In a world of Open Finance, are customers willing to share data? An analysis of the data-driven insurance business

Grassi

2024

Eurasian Bus Rev

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In the financial system, the customers’ willingness to share their data is pivotal, because otherwise, banks and insurance companies are powerless to build on customer data. The key step now is to understand whether there is such willingness and what form it takes. In this study, we investigate how willing customers are to share various kinds of data (on physical health, home, driving style, travel, family, social networks) with their insurance company, in return for different rewards (customised products and services, reduced insurance claims risk and insurance premiums adjusted to personal habits and behaviour). Applying the privacy calculus framework to 1501 responses in a web-based survey, we found that rewards, especially when financial, such as insurance premium benefits, play a pivotal role in driving customer decisions about sharing data. Furthermore, customers associate the data they are asked to share with different levels of privacy, influencing their willingness to share. We also found that, when customers are asked to share various kinds of data in return for different rewards, their own personal innovativeness comes into play. Our findings suggest that, in the data-driven insurance business, different rewards offered in return for specific types of data could help companies minimise the “data acquisition cost” and maximise the data collected. In the era of open data, insurers can explore the many opportunities for segmentation, but new kinds of financial exclusion could emerge, resulting in potential biases and thus misinterpretations should analytics and artificial intelligence models be built upon these premises.

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Dataset of an actual life-risk insurance portfolio

Cited by 5 publications

References 4 publications

Shortcuts for the construction of sub-annual life tables

Shortcuts for the construction of sub-annual life tables

Dataset of an actual motor vehicle insurance portfolio

In a world of Open Finance, are customers willing to share data? An analysis of the data-driven insurance business

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