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Learn more about model code and documentation translation using AI: Exploring a new use case of GenAI – Read the full material.
Tag Archive for: AI & ML
In today’s complex financial landscape, the integration of machine learning (ML) into risk management has unlocked unprecedented potential. At Probability & Partners, we’ve explored how Explainable AI (XAI) addresses different challenges across diverse risk domains, including credit risk, market risk, and operational risk.
The financial services industry stands on the verge of a profound transformation, driven by the rapid evolution of artificial intelligence (AI). Once seen as futuristic, AI is now becoming a cornerstone for businesses that want to enhance their decision-making processes, improve operational efficiency, and remain competitive.
We are excited to announce PALM (Probability Augmented Language Model), an AI-driven RAG system developed to enhance our internal consulting services.
• With real-time insights from internal data and regulations, PALM helps our team make faster, more informed decisions.
• Designed specifically for the risk management industry, PALM optimizes document analysis, reporting, and more.
How do we balance the risks and opportunities of AI? And how do we fully utilize what AI has to offer? A column by Pim Poppe and Kevin Rojer.
Discover how the EU Act impacts the financial sector. What are deadlines, challenges and opportunities with the AI Act for financial institutions?
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.