Tag Archive for: AI&ML

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.

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.

Svetlana Borovkova’s column in the Financial Investigator is about the ML application of pricing and hedging financial derivatives and reinforcement learning.

Svetlana Borovkova’s column in the Financial Investigator is about machine learning for market risk, neural networks, and the use of autoencoders as proposed by Prof. John Hull.

Svetlana Borovkova’s column about machine learning applications in finance, including banks, derivatives, market risk and quant investing.

This EBA discussion paper provides a good and comprehensive overview of the use of machine learning for credit applications.