Met oog op de komst van DORA heeft DNB een nieuwe Good Practice Informatiebeveiliging uitgebracht. Wat zijn de belangrijkste wijzigingen en waar kan je op letten?

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.

In this paper we describe AI fairness from a quantitative perspective, on the example of credit decision making – which candidates should receive a loan and which not – but the principles we will describe hold more generally in financial services. We explore the roots of bias in AI systems and present popular definitions of fairness adopted by the industry.

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.