Meet the Founder
Jenny Yu, founder and CEO of Himalaya Quantitative Solutions (HQS), is a seasoned executive who has 25 years of experience in AI/ML/model risk at major global financial institutions. She specializes in designing holistic AI/ML/model risk management frameworks and building validation/benchmarking tools to mitigate risk in a systematic and automatic way. Jenny leads HQS in creating a trustworthy AI tool for Generative AI risk management. She is recognized for empowering senior leadership by transforming complex data into actionable insights and enabling well-informed business decisions and seamlessly bridging the gap among intricate data,algorithms and business processes, and offering strategic solutions that resonate with stakeholders’ needs, pragmatically and effectively. Jenny holds a master of science in quantitative finance from UW-Madison.
Advisory Team
Our advisory team comprises seasoned financial experts who possess a wealth of experience in the industry. With a strong commitment to integrity and professionalism, we provide our clients with comprehensive and personalized financial solutions to help them achieve their long-term financial goals.
Why HQS?
We reveal the story behind your data -- to drive better business decisions by bridging the gap between it and your business processes and strategies.
We create initiatives (and provide the quantitative tools) to answer senior management concerns, leverage unused data, and provide strategic solutions.
With our years of experience at major financial institutions, we deeply understand what problems quantitative processes can best solve and how to implement strategies to improve business effectiveness and efficiency.
We have proven success in translating regulatory guidance into the policy and designing, implementing, and maintaining model risk management framework for both the second (risk) and the third lines (audit).
Through hands-on management of over 100+ financial models used in risk management, investment, we have board perspectives and provide a holistic modeling process.
With HQS, you will now have access to the expertise and tools that the very largest financial institutions routinely use, and will be tailored to your institution's needs.
Team Experience and Success Stories
High interest rate modeling challenge
A bank usually uses a model to estimate the expected return when purchasing a fixed-income instrument. We identified the critical model assumptions and their impact on model output including the expected returns. The returns from different interest rate models diverge dramatically due to the rapid increase in interest rates. We communicated the finding with management and proposed solutions, such as considering a model risk cushion in the pre-trade guideline.
Award-winning idea to help increase profitability
Created an award-winning idea to help the business increase profitability. Most banks have estimated sophisticated risk measurement for regulatory requirement. We were able to help one bank to connect the dots, balance risk/cost and return/profit, improve business efficiency and profitability, and won the 2013 BNY Mellon “Global Innovation Award".
True stories
behind data
Revealed the true stories behind data: what data can tell us and what it cannot. The bank did not have sufficient mortgage default data by itself, so it used nationwide data. The model from the national data could reasonably help the bank rank-order the riskiness of loans. But when the bank estimated credit loss CECL, it needed to tune the probability of default or loss given default to ensure they reflected bank's portfolios. There is a similar application for mortgage prepayments.