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SUMMARY:Tutorial "Rechnungslegung von Versicherungsunternehmen" Modul 3: Non-Life Rückstellungen in der unternehmensrechtlichen Versicherungsbilanz
DESCRIPTION:Beinahe jeder Geschäftsfall\, jede Entscheidung hat eine Auswirkung auf die Bilanz oder Gewinn- und Verlustrechnung. \nIn diesem Kurs und den anschließenden interaktiven Workshops entwickeln Sie ein klares Verständnis für die Grundlagen des Jahresabschlusses und für die Zusammenhänge von Bilanz und G&V. Dabei erkennen Sie die buchhalterischen Auswirkungen verschiedener Geschäftsfälle und sind in der Lage die offengelegten Finanzinformationen des Unternehmens besser zu beurteilen. \nAnmeldeschluss: 16.03.2022
URL:https://avoe.at/event/tutorial-rechnungslegung-von-versicherungsunternehmen-modul-3-non-life-rueckstellungen-in-der-unternehmensrechtlichen-versicherungsbilanz/2022-04-01/
LOCATION:Online/Streaming
CATEGORIES:Österreichische Gesellschaft für Versicherungsfachwissen (GVfW)
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SUMMARY:EAA Web Session: Machine Learning and Anomaly Detection
DESCRIPTION:Machine Learning (ML) allows computers to process data\, analyse it in real time and learn and make decisions based on data. Diverse applications from self-driving cars to chess computers have successfully relied on ML.\n\nWhile the insurance industry is not necessarily known for being particularly innovative\, insurance companies are increasingly embracing approaches commonly used in ML to address business challenges in different areas. Actuaries and data scientists apply ML to claim management\, underwriting or customer service.\n\nNowadays\, both data and models can be processed much faster than before which means data-driven approaches to actuarial modelling are being increasingly adopted by the insurance companies. The amount of data being used by insurance companies for different purposes has increased exponentially. As such\, it is becoming more difficult for actuaries to identify anomalies in data\, models and outputs. For example\, some insurers apply Least Square Monte Carlo methods to derive their Net Asset Value and Best Estimate Liability proxy models. These models take a huge amount of data to perform complex calculations. It is not possible for actuaries to understand bad data and model behaviour by using traditional methods given the amount of data involved.\n\nIn this web session\, we are going to discuss how techniques commonly used by data scientist in ML applications can help actuaries detect/remove bad data and significantly improve forecasting abilities of modern actuarial models.\n\nRegistration deadline: 30 March 2022
URL:https://avoe.at/event/eaa-web-session-machine-learning-and-anomaly-detection/
LOCATION:Wien
CATEGORIES:European Actuarial Academy (EAA)
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