Upon the request of the speaker, no recording is available for this lecture.
The Department of Finance and Risk Engineering welcomes Frederic Siboulet, Adjunct Professor, NYU Tandon School of Engineering, FRE to the BQE Lecture Series.
Machine Learning in Financial Services
Artificial Intelligence and Machine Learning are having a broad impact on the financial services industry, ranging from instrument valuation, risk & hedging, portfolio construction, the financial operation to client digital experience.
- Market participants' tangible changes already include for example:
- Alternative data, support of new formats such as multimedia and IoTs, ubiquitous natural language processing, with new information signals across asset classes,
- Research process at speed and scale to capture early investment opportunities for alpha generation
- A new continuum field between quantitative and fundamental investment – « quantamental »
- New families of algorithms - Neo-Modern Portfolio Theory (NMPT) - enabled by an abundance of data and computing capacity
- The transformation of the client interface with an integrated view of the client’s profile for an AIML experience
- Natural Language Processing for operational risk management
The impact of machine learning is compounded by other technology factors, such as the rise of hybrid and multi Clouds, even splintering into specialized architectures now optimized for big-data and high-dimensional problems enabling solutions that would have been unthinkable a decade ago, cost-effectively and on an industrial scale.
The diffusion of open source software is also a source of creativity, which accelerates the transformation of all industries. These factors create a rock bottom barrier to entry, threatening our clients who are compelled to transform in order to stay relevant.
Frederic Siboulet has been teaching at Tandon since 2014, while he has been a Managing Director with Big 4 consulting firms in New York. His expertise lies in commercial banking, investment banking, investment management, market and credit risk, as well as model development and validation. He has been leading large engagements for SR11-7, CCAR, CECL, IFRS9 in banking, and PPNR for derivatives and structured products in trading. He also implemented front-to-back trading, risk, and operating systems for fixed income and equity derivatives with Murex, as well as enterprise risk systems with Algorithmics. He developed artificial intelligence and machine learning modeling, data, and technology practice, specifically with deep and reinforcement learning. His experience spans the sell-side to the buy-side, for multiple asset classes, cash, and derivatives, such as in wholesale lending, pricing and trading, position management, risk management, cash management, portfolio optimization, counterparty credit risk, margin and collateral, exchanges, clearing and settlement, market and credit risk.