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Abstract The Capitation Payment Unit (CPU) financing mechanism constitutes more than 70% of health spending in Colombia, with a budget allocation of close to 60 trillion Colombian pesos for the year 2022 (approximately 15.7 billion US dollars). This article estimates actuarially, using modern techniques, the CPU for the contributory regime of the General System of Social Security in Health in Colombia, and compares it with what is estimated by the Ministry of Health and Social Protection. Using freely available information systems, by means of statistical copulas functions and artificial neural networks, pure risk premiums are calculated between 2015 and 2021. The study concludes that the weights by risk category are systematically different, showing historical pure premiums surpluses in the group of 0–1 years and deficits (for the regions normal and cities) in the groups over 54 years of age.

The Capitation Payment Unit (CPU) financing mechanism constitutes more than 70% of health spending in Colombia, with a budget allocation of close to 60 trillion Colombian pesos for the year 2022 (approximately 15.7 billion US dollars). This article estimates actuarially, using modern techniques, the CPU for the contributory regime of the General System of Social Security in Health in Colombia, and compares it with what is estimated by the Ministry of Health and Social Protection. Using freely available information systems, by means of statistical copulas functions and artificial neural networks, pure risk premiums are calculated between 2015 and 2021. The study concludes that the weights by risk category are systematically different, showing historical pure premiums surpluses in the group of 0–1 years and deficits (for the regions normal and cities) in the groups over 54 years of age. [ABSTRACT FROM AUTHOR]

Debris flow risk is growing with the current increases in landscape exploitation and extreme precipitation events associated with global warming. Insurance is an efficient approach to cope with disasters. Based on the quantitative assessment, the debris flow hazard was obtained by a dynamic run-out model (FLO-2D), and the pure risk premiums of debris flow disaster were rating in a high-risk region (Anzhou) in Sichuan, China. Simulation results indicated that disastrous debris flows would reoccur every 5 years on average under the contemporary climatic and geological conditions of the study area. The mountain region experienced a wider area affected by debris flows than plains and hills; however, its area affected by low debris flow depth (d < 0.1 m) accounts for a larger proportion (56.69% and 22.60%, respectively). The probability of sub-basins with basin slopes of 5°–10° experiencing serious debris flows was pretty close to the probability of sub-basins with slopes of 25°–35°, which are considered to be susceptible to trigger debris flows. Furthermore, for relatively flat sub-basins (slopes < 25°), the part closer to source was more susceptible to higher flow depth. Risk assessment results showed that the significant spatial difference between the distribution of debris flow risk and hazard largely linked to the distribution of at-risk element (buildings). The pure risk premium of Anzhou was rated as 0.69–25.48‰ depending on the proportion of at-risk buildings to insured buildings, which highlights the need to encourage or require more low-risk and secure buildings to participate in insurance schemes to share the debris flow risk of the entire region. [ABSTRACT FROM AUTHOR]

Insurance companies frequently ask their actuaries to do calculations of the probability of the company being ruined. These calculations are based on complex data analysis of the last insurance period, which requires building mathematical models and using the data as an input parameter in these models. For this purpose, an algorithm, which presents a simplifed mathematical model and provides approximate results of the outcomes (later used for managerial decisions), was prepared. Modeling of diferent outcomes based on various different inputs shows that the probability of the company to become ruined is inversely proportional to the written premium. The algorithm developed in this paper is illustrated with tables. The model is presented to the reader in a way that the reader can reproduce the calculations and build a custom data model. [ABSTRACT FROM AUTHOR]

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