As a result of COVID-19, the insurance industry is being affected through the combination of lockdown measures, changing product demand, increased mortality, health & event cancellation claims, & volatile financial markets, among others.
With hindsight, the UK regulators’ focus on operational resilience, as opposed to simple disaster recovery planning, has thus proven to be prescient. It seems likely that regulators will expect firms to report – in some form or other – on their takeaways from the pandemic experience.
In this webinar, we speak with Cresendo Advisors to obtain input on the findings from their Lessons Learnt Operational Resilience benchmarking study.
Crescendo Advisors conducted structured interviews with a selection of risk professionals from insurance firms to understand the impact that Covid-19 has had on their firms & to assess how they responded, what they did well, what lessons they learned, & changes they anticipate as a result of the pandemic experience.
We explore among other:
- The impacts on insurance businesses’ operations
- After the pandemic experience, what regulators are likely to expect from firms
- The challenges that new world of working from home will pose for the design, implementation and documentation of an appropriate functional control environment; and
- The likely change the cost-benefit dynamic of outsourcing / off-shoring so that many firms could find it beneficial & desirable to bring activities back in-house.
Webinar Speakers: Isaac Alfon Founder & Managing Director at Crescendo Advisors, Shirley Beglinger Advisory Board Member at Crescendo Advisors, & Helio Correa Head of Risk & Compliance at NHBC (participated in the research that will be discussed).
The event was facilitated by Valerie du Preez.
Actuaries and their organisations are facing significant challenges in an ever changing world, especially in the midst of a global pandemic.
A brief history of Insurtech and how it is likely to develop in the future. We also explore the application of image, video and audio in the financial services industry and in insurance.
A huge increase in data generation, data capture & data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value their data can provide; & the technologies available to do that.
Actuartech and Montoux are collaborating on a content series that digs into the heart of what the modern actuary looks like, the challenges and opportunities facing the profession today, and what the actuaries of tomorrow could look like.
AI has become more and more popular within the financial industry, and many insurers have started out on their AI journey. We will talk about some of the worries around delegating decisions to machines, and how to overcome some of these challenges.
The challenges faced at the moment given the global pandemic is causing actuarial teams to change their operating models significantly. How ready have we been for this change and what issues have we been experiencing?
Explainable Machine Learning Machine learning techniques have become more and more popular within the financial industry mainly because of the potential to capture complex interactions from data the potential for better predictive models than traditional statistical models the ability to capture non-linear interactions within a range of inputs
A huge increase in data generation, data capture and data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value that their data can provide; and the technologies available to do that.
We explore opportunities created by new Insurtech innovations as well as look at how companies are responding.Insurers have the opportunity to optimise their processes and create new sources of revenue by rethinking their traditional business models with the use of modern day technologies.
IFRS17 has been deferred by another year.... The IASB decided that IFRS 17 will come into force on January 1, 2023. Join our webinar as we look at what this means for insurers. We will look back at recent progress made on IFRS 17 implementation by insurers. We will also look at the opportunities available to insurers to make the most of the extra year.
Methods used in non-life pricing are evolving at a fast pace and more advanced actuarial and statistical techniques are being used in pricing, competition analysis and profitability analysis.
How can life insurers address low persistency? How can data and analytics help? Join our webinar on Friday 12 March 12pm as we discuss how the full cycle of actuarial analysis is evolving
A huge increase in data generation, data capture and data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value that their data can provide.
Improvements in computational power has given rise to the use of data science and artificial intelligence techniques in a wide variety of areas, including finance, driverless cars, image detection, speech recognition etc. This is directly impacting business practices within the financial services through its application within banking, insurance and asset management.
In the current economic climate, insurers need to improve their processes continually – making them more efficient and cost-effective while maintaining the agility to deal with new requirements. At the same time, technological change is providing new ways of achieving these objectives. In particular, the term ‘robotics’ appears to be used everywhere, and it is important to grasp the impact and potential use of these new technologies.
Listen to Valerie du Preez, founder and Managing Director of Actuartech, on the panel for Legerity's IFRS 17 webinar discussing, "Can IFRS17 help deliver digital transformation?".
In a world of high volume and varied datasets, data science techniques can add valuable tools to an actuary’s toolkit to provide actionable insights from data.
Sophisticated machine learning models have the potential for high predictive accuracy but their complexity may sometimes result in black box models, which, in some cases, may appear to be a trade-off between accuracy and interpretability.
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