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


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