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Special Newsletter on EAA Web Sessions - Benefit from
our Early Bird Rates
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Book now and save money!
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Benefit from discounts for many EAA web sessions taking place in
May and June with early bird rates today, on 20 April and on 24
April. These online trainings cover topics such
as
- How to Read the New IFRS Balance Sheet
for Insurers,
- Imbalanced Classification: Problems
& Solutions with Use Cases and
- ML Explainability in Actuarial Data
Science: A Practical Primer.
Furthermore, you are invited to visit our website for all
published online trainings here.
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Web Session: "How to Read the New IFRS Balance Sheet for
Insurers" on 23 May 2023, 9:00-12:15 CEST
Early Bird discount until today
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The goal of this three-hour web session is to provide
participants with a comprehensive introduction on the new IFRS
reporting requirements for insurance contracts after go-live of
IFRS 17. Focus will be the illustration of the new reporting
requirements of IFRS 17 to "demystify" the new presentation
requirements on the IFRS balance sheet and the statement(s) of
financial performance (Profit and Loss as well as Other
Comprehensive Income). The web session will also briefly compare
key aspects of the new reporting requirements to today's IFRS
4-reporting practice, contain a brief summary of the main
information which can be found within the new IFRS 17 reporting and
cover the different aspects for primary and reinsurance related
business.
Overall, the goal is to enable participants to understand the IFRS
17 reporting and help transferring the reporting requirements into
the specific situation of the participant. It is thus intended to
prepare participants for implementation, testing, reviewing and
consulting with management, accounting and auditors.
Your early-bird registration fee is € 150.00 plus
19% VAT until today. After this date, the fee will
be € 205.00 plus 19% VAT.
further details
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Web Session: "Imbalanced Classification: Problems &
Solutions with Use Cases" on 1 June 2023, 9:00-14:00
CEST
Early bird discount until 20 April 2023
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During the past decade, supervised classification problems have
been identified in several actuarial fields, such as risk
management, projection modeling, fraud and anomaly detection, etc.
In many of these problems, the respective classification task is
subject to a highly imbalanced dataset, i.e., the number of
instances of the relevant class is extremely small in comparison to
the total number of instances. Classical supervised machine
learning frameworks can be misleading (in case of using an
inappropriate evaluation metric) or ineffective (in case of using
inappropriate classifiers) in such situations.
In this web session, we will present several techniques to tackle
these issues. More specifically, external approaches (data
preprocessing, such as over- and undersampling procedures) as well
as internal approaches (modification of classifiers, e.g., balanced
versions of random forests and support vector machines) will be
discussed. After a concise introduction to imbalanced
classification and the techniques above, we will turn theory into
practice by implementing entire machine learning workflows in
Python and R for two real-world use cases: churn prediction and
fraud detection.
Your early-bird registration fee is € 200.00 plus
19% VAT until 20 April 2023. After this date, the
fee will be € 270.00 plus 19% VAT.
further details
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Web Session: "ML Explainability in Actuarial Data
Science: A Practical Primer" on 5/6 June 2023, 9:00-16:30
CEST
Early bird discount until 24 April 2023
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These days, nobody disputes the profound impact and
yet-untouched potential of Machine Learning and Artificial
Intelligence anymore. Yet, in the actuarial sciences, these
breakthrough possibilities are hampered by regulation, the need for
numerical confidence and insight into model decision making, the
latter being subsumed as a "black box problem". Thus, the quest for
explainability is in fact much more pressing than in any other
industry.
This upcoming seminar on ML explainability aims to provide
insights into the areas of unsupervised learning, supervised
learning and artificial neural nets via model-agnostic
explainability approaches, while providing opportunities to try out
the methods yourself!
Your early-bird registration fee is € 600.00 plus
19% VAT until 24 April 2023. After this date, the
fee will be € 780.00 plus 19% VAT.
further details
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