IRisk Lab Research
IRisk Lab research projects collaborate with company partners to solve real world issues by applying mathematical models and statistical methods. This also provides students valuable opportunities to engage with industry and apply their knowledge to real world scenarios.
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Recent Projects

Carbon Option Pricing under a Stochastic Volatility Framework
As the world transitions toward a low-carbon economy, carbon markets have emerged as critical tools in the fight against climate change…

Climate Data Discovery and Consolidation
As data-driven decision-making becomes increasingly vital in addressing climate-related challenges…

PCMI Warranty Data Innovation Project
This project engages students from the University of Illinois in a practical, hands-on experience with PCMI…

Model Governance and Ethics for GenAI Models
Transformer-based Large Language Models (LLMs) have significantly accelerated…

Decentralized Trading and Risk in the Crypto Market
The rapid growth of blockchain technology and the expanding cryptocurrency market…

Introduction to Monte Carlo Simulation
Monte Carlo simulation is a technique for predicting outcomes involving uncertain events…

RGA Financial Models
This R&D project explores the development and assessment of various data-driven financial cash models…
Past Collaborations

Federated Learning for Facilitating Privacy Preserving Collaboration
Due to privacy and data confidentiality concerns, today’s insurance industry is rife with the protectionism…

COUNTRY Financial Business Owner’s Policy (BOP) Loss Predictive Model
For the insurance industry, the potential to understand customers and businesses using new dimensions represented…

Building an NLP-Powered Repository and Search Tool for Cyber Risk Literature Project
Since the time when cyber insurance was first introduced to the market, there has been…

Insurtech Innovation via Natural Language Processing (NLP) Project
For the insurance industry, the potential to understand customers and businesses using new dimensions represented…