Tag Archive for: AI&ML

The AI revolution introduces significant new risks around model use centered around interpretability, biases, stability, lack of supervision and inaccurate training sets. How can you update your model risk framework to adequately deal with these risks?

Discover how to integrate generative AI in finance and risk management, enhancing efficiency across the AI maturity curve. Ideal for professionals at all levels.

LLM implementations are rapidly emerging in finance, but they are not perfect. How can you finetune your LLM address some of these challenges? A column by Svetlana Borovkova

In this paper, I give a bird’s-eye view of Large Language Models and outline the most significant issues related to their applications in financial services. I will discuss potential use cases, LLMs’ limitations, and the challenges associated with their applications. The objective is to provide the reader with understanding of various aspects of LLMs, placing them in the context of financial institutions. Additionally, I will discuss ways of implementing LLMs in finance-related areas, outline potential dangers and pitfalls, and explore emerging strategies of overcoming these challenges.

Can we ensure fairness and explainability for AI and ML in insurance? Tools and techniques for ensuring explainable ML, bias measurement and mitigation.

Ethics, integrity, internal control and goverance of AI and ML in applications for the insurance sector, a column by Amba Zeggen.

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.

Svetlana Borovkova’s column in the Financial Investigator is about the relationships between news sentiment & corporate bond yields, and ESG scores & corporate bond yields.

This paper investigates the relationship between news sentiment and corporate bond yield spreads, including assymmetry and sentiment-based bond investing.