Model Governance in the Age of Data Science and AI
With innovations in hardware, algorithms and large datasets, the use of Data Science and Machine learning in finance is increasing. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling models. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms exploring novel modeling methods to augment their traditional investment and decision workflows. While there is significant enthusiasm, model risk professionals and risk managers are concerned about the onslaught of new technologies, programming languages and data sets that are entering the enterprise. With very little guidance from regulators on how to govern the tools and the processes, organizations are developing their own home-cooked methods to address model governance challenges. In this workshop, we aim to bring clarity on some of the model governance challenges when adopting data science, AI and machine learning methods in the enterprise. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model governance framework when incorporating data science techniques and AI methodologies.
Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity, a data and Quantitative Analysis Company and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. He has also consulted with many organizations in establishing model governance practices. Prior to starting QuantUniversity, Sri has had significant analytical applications at Citigroup, Endeca, MathWorks and has consulted to more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics and big data in the industry and at Babson College, Northeastern University and Hult International Business School. Many of his students work in Data science roles at Fidelity, Santander, Wellington, GMO, State Street etc. Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA with a focus on Investments from Babson College.