ABOUT THE RISK AND INSURANCE STUDIES CENTRE (RISC) Our vision is for risk mastery to advance universal progress through interdisciplinary knowledge, innovative education, and dialogue. We aim for a Risk Management & Insurance (RMI) field grounded in translational, rigorous scientific research, attracting and nurturing top emerging talent, and collaborating with diverse stakeholders for a safer, more sustainable, and prosperous future. Our mission is to create and mobilize interdisciplinary knowledge to advance the field of Risk Management & Insurance and to develop and establish creative education and training programs that empower individuals to navigate and master the complexities of risk.

More specifically, with the world’s finest research experts on board and a unique place in the RMI industry, our overarching mission involves the creation of transformative scientific research and its timely translation into tangible, real-world applications within the RMI industry. Conducting comprehensive and rigorous scientific research that addresses industry challenges and paves the way for better systemic risk management solutions. Cultivating emerging talent to be ready to step into rewarding RMI jobs and become the risk leaders of tomorrow.

This is a four-month paid research position with the possibility of developing into a publishable article. Ed Furman of RISC Foundation and York University and Dr.

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Mnif and will have access to a collaborative network spanning industry and academia. Financial language models hallucinate. Most existing work catches these errors after the fact.

This project asks a harder question: can we predict that a model is about to hallucinate before it generates a single token?

The Project

You will work on a three-part system applied to Canadian bank filings and financial disclosure analysis, where a missed hallucination has real regulatory and liability consequences.

Risk scoring: build a scorer that examines the incoming question, retrieved documents, and internal model signals to output a probability that the response will contain an error Consistent confabulation of regulatory ratios or hallucinated dates under sparse context are not random failures -- they are structured, and can be characterized Statistical guarantees: wrap the system in a formal guarantee, that is for any flagging threshold, bound the fraction of hallucinations missed, with a proof that holds regardless of query distribution shift We are looking for a strong researcher who sits at the intersection of statistics, mathematics, and computer science. Graduate training -- Ph.D. level preferred -- in statistics, mathematics, computer science, or a closely related field Proficiency in Python and deep learning frameworks, e.g. Solid understanding of neural network architectures including transformers Working knowledge of linear algebra and statistical testing An asset:

📌 Research Assistant - Financial Language Models (Toronto)
🏢 RISC Foundation (Risk and Insurance Studies Centre)
📍 Toronto