IRisk Lab Spring 2026 Projects

This R&D project explores the development and assessment of various data-driven financial cash models to manage RGA’s global financial service products including longevity swaps, asset-intensive transactions, and pension risk transfers. Transaction-specific data runs the spectrum from voluminous to sparse. Transactions typically range from $100 million to $10+ billion.

Participants will apply actuarial science and data science to develop various proxy models and assess underlying assumptions. Models are used to monitor performance, risk profiles, manage capital, and provide insights into business management.

The IRisk team will collaborate with RGA Global Financial Services (GFS) Valuation Team Europe-Middle-East-Africa (EMEA) actuaries led by former ARMS instructor, Tim Cardinal. Spring 2026 will focus on learning the business, models, and data, developing problem definitions, and building prototypes. Spring 2026 will focus on operationalizing prototypes and learning insights into models’ strengths and weaknesses as they are applied to recent/new transactions. Without going into details, the work will be engaging, challenging, and exciting.

Students will be required to sign a non-disclosure agreement (NDA). In your application, please indicate your interest in internships and co-ops. Ideally, we would like 1-3 of the IRisk student team to intern during summer 2026 and 1-2 students to co-op in fall and/or spring 2026.

RGA is one of the largest global life and health reinsurers and is #223 on the Fortune 500 with offices in dozens of countries around the world from St. Louis and Toronto to London, Dublin, Paris, and Milan to Hong Kong, Shanghai, Beijing, Tokyo, Mubai, and Dubai.

Supervisors: Xiaochen Jing, Frank Quan, Tim Cardinal, RGA GFS Valuation EMEA team

On Ethereum, trades are not executed instantly. When a user submits a transaction, it enters a waiting area, and whoever builds the block gets to decide which transactions are included and in what order. This creates opportunities for profit through transaction reordering, a phenomenon known as Maximal Extractable Value (MEV).

MEV is not inherently malicious, but some forms of it directly harm users. A typical example of MEV is the sandwich attack. A sandwich attack occurs when an attacker sees a user’s trade before it is finalized and places one trade immediately before it and another immediately after it. The attacker profits from the price movement caused by the victim’s trade, while the victim receives a worse execution price. Economically, this behaves like a hidden fee on traders: one that varies by token liquidity, market volatility, and where and how the trade is ordered.

In this project, we will identify sandwich attacks directly from on-chain data, estimate how much value is extracted from users, and analyze when sandwich attacks are most severe (trader characteristics, platform liquidity, exchange protocols, builder concentration, etc.). We will then focus on mitigation. In recent years, several approaches have been proposed to reduce sandwich attacks, including private transaction submission, alternative auction rules, and mechanisms that redistribute MEV back to users.

Throughout the semester, we will engage with recent research on decentralized exchanges and sandwich attacks, collect and analyze on-chain transaction data, identify attack patterns, and evaluate the strengths and potential vulnerabilities of different decentralized exchange designs and operating mechanisms.

Supervisor: Xiaochen Jing
Graduate Supervisor: Zhonghe Wan