KEY CONTRIBUTIONS OF OWN RISK SOLVENCY ASSESSMENT (ORSA) TO THE IMPROVEMENT OF THE ERM OF INSURANCE COMPANIES: A PRACTICAL AND INTERNATIONAL VISION
Bibliographic record
Abstract
espanolEIOPA (European Insurance and Occupational Pensions Authority), NAIC (National Association of Insurance Commissioners), OSFI (Office of the Superintendent of Financial Institutions) junto con otros reguladores a nivel mundial estan desarollando un nuevo requisite regulatorio denominado ORSA (Own Risk Solvency Assessment). ORSA ha sido disenado para mejorar el proceso de gestion, valoracion y reporting de los riesgos a nivel global (ERM) por parte de las companias de seguros, presentado una especial atencion a la optimizacion del proceso de toma de decisiones relacionando el nivel de solvencia de la compania y su riesgo de exposicion. El objetivo de los reguladores es proporcionar una mayor estabilidad al sector asegurador estableciendo una mejora proceso de gestion global del riesgos (ERM) desde el punto de vista regulatorio. Esta mejora incluye aspectos como la inclusion en el proceso de la fijacion del apetito de riesgo de cada compania, proceso de validacion del capital de solvencia mediante la utilizacion de diversas metodologias como backtesting, stress testing, proyeccion de escenarios e incluso la inclusion de tecnicas como reverse testing. En este articulo las principales diferencias y similitudes entre los principales reguladores es descrita, asi como las principales contribuciones de ORSA son analizadas, mostrando un especial interes al proceso de backtesting con el animo de validar la valoracion desarrollada en relacion al capital de solvencia requerido. Por ultimo, se desarrollaran dos ejemplos practicos en el objetivo de analizar de forma practica el proceso de backtesting presentado en el articulo desde el punto de vista teorico. EnglishEIOPA (European Insurance and Occupational Pensions Authority), NAIC (National Association of Insurance Commissioners- US regulator) OSFI (Office of the Superintendent of Financial Institutions -Canadian regulator) and other regulators are working on a new regulatory requirement called ORSA (Own Risk Solvency Assessment). ORSA is designed to improve the risk management, reporting and assessment process of insurance companies, especially in the decision-making process with regard to the level of solvency according to their risk exposure. In this presentation the differences and similarities between the jurisdictions are described. The objective of the regulators is to improve the stability of the insurance sector establishing an adequate risk management requirement that includes important aspects such as definition of the risk appetite, validation of the solvency requirement using, for example a backtesting methodology, stress testing, scenarios projection and the inclusion of technique such as reverse testing. In addition, the analysis of the main contributions of ORSA for the insurance companies is developed, highlighting points such as stress, scenario projection and the back-testing process with the aim to accurately assess the solvency capital requirement according to the situation of the company. Practical examples and real-life business cases will be provided to illustrate the process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".