Machine Learning for Market Data Anomaly Detection & Gap Filling

Numerix Webinar Featuring Intesa Sanpaolo & Deepfin Labs

Historical market data is a critical input for many market risk calculations, and an institution’s daily calculations require enormous volumes of data.  However, the quality of the calculations is directly linked to the quality of the underlying data, so if data is anomalous or missing, the risk measures will be incorrect and misleading, and risk management decisions will be impacted.

Can Machine Learning (ML) techniques be utilized to identify anomalous data, and to fill in gaps in the data where is it missing, to improve the quality of these risk calculations?  How proficient are these techniques at performing this task, and how much human intervention is still required?

Join presenters from Intesa Sanpaolo, Deepfin Labs and Numerix , as they shared their approaches to this task, along with results from preliminary testing.

During the webinar, the presenters covered:

  • Why anomaly detection and gap filling are crucial for risk management
  • Two ML techniques for anomaly detection with application to interest rate curve data
  • One ML technique for gap filling with FX forward data across multiple currency pairs
  • Results from preliminary testing
  • An interview discussing the similarities, differences, and novelty of the different techniques

Featured Speakers:

Manola Santilli, PhD, Quant Senior Specialist in IMA Market Risk, Market and Financial Risk Management, Intesa Sanpaolo

Manola joined the Market and Financial Risk Management area of Intesa Sanpaolo in 2015 as a market risk analyst in the Internal Model Market Risk Office. She works in market data management, with a focus on market data collection and statistical analysis of historical time series across all asset classes, market risk metrics monitoring, analysis and implementation of FRTB.

Previously she worked as a quantitative analyst in Société Générale.

She holds a PhD in Statistical Economics, with a thesis on advanced stochastic volatility models for derivative pricing, and an MSc in Economics and Finance.

Marco Scaringi, Quant Specialist, Financial and Market Risk Management, Intesa Sanpaolo

Marco Scaringi joined the Financial and Market Risk Management area of Intesa Sanpaolo in 2017 as quantitative analyst. His work covers pricing and risk management of financial instruments across all asset classes, with a focus on model validation, model risk, fair value adjustments, interest rate modelling, funding and counterparty risk and prudent valuation. His research focuses on interest rate models and XVAs, financial bubble analysis, portfolio optimization and Financial Benchmarks transition.

He holds an MSc in Theoretical Physics from University of Milan, with a thesis on advanced statistical mechanics techniques applied to the description and detection of financial bubbles through optimization heuristics. He also holds a post lauream degree Executive Course of Quantitative Finance from MIP, Graduate School of Business, Polytechnic of Milan, with a thesis concerning interest rate and XVA modelling.

Marco Bianchetti, PhD, Head of IMA Market Risk, Market and Financial Risk Management, Intesa Sanpaolo, & Adjunct Professor, Department of Statistical Sciences "Paolo Fortunati", Università di Bologna

Marco holds an MSc in Theoretical Nuclear Physics (1995) and a PhD in Theoretical Condensed Matter Physics (2000) from Università degli Studi di Milano. He joined the front office Financial Engineering team of Banca Caboto (now IMI Corporate and Investment Bank Division of Intesa Sanpaolo) in 2000, where he developed pricing models and applications for the fixed income trading desk. In 2008 he moved to the Financial and Market Risk Management of Intesa Sanpaolo, where in 2015 he was appointed head of Fair Value Policy, developing the global fair/prudent/IPV policies and the valuation risk management framework of Intesa Sanpaolo Group. In 2021 he was appointed head of IMA Market Risk, in charge of the internal model for market risk.

His work covers pricing and risk management of financial instruments, with a focus on market risk, valuation risk, interest rates, XVAs, quasi-Monte Carlo, financial bubbles and portfolio optimization. He is the author of a few research papers, adjunct professor at Università di Bologna since 2015 and at Università di Torino since 2018, and a frequent speaker at international conferences and trainings in quantitative finance and risk management.

Ron Coleman, PhD, Chief AI Officer, Deepfin Labs, & Professor of Computer Science, Marist College

Dr. Ron Coleman is Chief AI Officer of Deepfin Labs, a fintech incubator focused on using Artificial Intelligence to advance capital markets.  He is also a Professor of Computer Science at Marist College, specializing in Artificial Intelligence and Machine Learning.

Prior to joining Marist College, Dr. Coleman was a vice president in risk management technology at Citigroup and co-founded Informeta, a high-tech startup. Prior to that he spent four years as an Advisory Engineer for IBM in the Supercomputing Systems Lab, where he was a part of a team that built a version of the supercomputer that would become Deep Blue, the first computer to win a chess match against a reigning world champion.

Ron holds a PhD in Computer Science from New York University’s Tandon School of Engineering, and he previously earned his BS in Computer Science from The City College of New York’s School of Engineering.

Pascal Lemieux, Director of Financial Engineering, Numerix
Pascal Lemieux is Director of Financial Engineering in EMEA at Numerix, where he works on the development of fintech software solutions. His primary focus is leveraging Numerix’s proprietary SDKs to engineer software for performing risk and valuation calculations for trading firms and financial institutions across Europe. Mr. Lemieux is also a principal contributor to the creation of the Machine Learning framework at Numerix. He has more than 15 years of professional experience in quantitative finance, working for leading firms such as J. P. Morgan and Royal Bank of Canada (RBC) in London, UK, and Société Générale in Paris, France. Mr. Lemieux holds a BEng degree in Engineering Physics and an MSc in Financial Mathematics. He is also the author of scientific publications on semiconductor modelling.

Moderator: James Jockle, Chief Marketing Officer & Executive Vice President at Numerix

James Jockle leads the company's global marketing efforts, spanning a diverse set of solutions and audiences. He oversees integrated marketing communications to customers in the largest global financial markets and to the Numerix partner network through the company's branding, electronic marketing, research, events, public relations, advertising and relationship marketing.

Prior to joining Numerix, he served as Managing Director of Global Marketing and Communications for Fitch Ratings. During his tenure at Fitch, Mr. Jockle built the firm’s public relations program, oversaw investor relations and led marketing and communications plans for several acquisitions. He also oversaw the brand development of a new company dedicated to the enhancement of credit derivative and structured-credit ratings, products and services. Prior to Fitch, Mr. Jockle was a member of the communications team at Moody's Investors Service.


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