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Can we ensure fairness and explainability for AI and ML in insurance? Tools and techniques for ensuring explainable ML, bias measurement and mitigation.
Large Language Models are reshaping Financial Services.
The aim of this paper is to extend Variational AutoEncoders (VAE) to allow for heavy tailed distribution of the latent space and apply them to the problem of market risk of large portfolios.
In this paper, we discuss how reinforcement learning can be successfully applied to hedging of options and show that these machine learning algorithms can “transfer” knowledge obtained from simulated data to the real-world option trading environment.
In this whitepaper, we describe the model risk management cycle and crucial elements which are needed for successful model management. After reading this whitepaper, you will develop an understanding of the key model development steps and key model risks for these steps, as well as possible practical solutions to address the risks.
There is no doubt that the world is changing due to the COVID-19. There are several prominent new trends emerging now, and they will define the post-corona world. One of these new trends is heightened attention to sustainable innovation. It is likely that antiquated, less sustainable companies will partially disappear, giving way to more environmentally and socially conscious organizations. This is partially due to the fact that sustainability requirements are important part of governments’ rescue packages. This is a far-reaching and long term trend; however, the question arises, how sustainable companies have withstood the crisis so far, compared to less sustainable ones? This is the question we attempt to answer in this paper.
With this paper, Probability and Partners would like to assist financial institutions in understanding the implications the ECB guide has for them, as well as help them prepare for the future where climate-related
and environmental risks are a part of everyday reporting.
In this paper, we discuss the three main emerging societal trends that we have identified: social transformation, digitalization and socially responsible innovation – and the effect of these trends on companies and sectors. In our high-level qualitative analysis we take into accountissues such as consumer behavior, business opportunities and government support.
In this note we briefly review the main prepayment modelling methodologies, main factors affecting prepayment risk and outline some other related issues such as hedging of prepayment risk and the associated model risk.