How to Detect Anomalies Using Machine Learning

How to Detect Anomalies Using Machine Learning
Thought Leadership Webinar:  Anomalies usually indicate that something has gone wrong: errors, system failures and fraud are all anomalous events. In this webinar, Jesús Calderón, MD at Gravito, a firm that specializes in applying data science methods to risk management problems, will discuss how to apply unsupervised machine learning methods to detect anomalies, as well as use cases where these techniques can be applied.


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Webinar Experts CRL Credits


Presented By:

Jesus Calderon


May 29, 2019


10:00 a.m. - 11:00 a.m.

Session Length:

60 minutes

About This Webinar

As risk managers, we spend a large portion of our time analyzing rare events. Anomalies are a specific type of rare events: not only do they occur with low frequency, but they deviate from other events to create the suspicion that they were created by a different mechanism. Anomalies are interesting because they typically indicate that something has gone wrong: errors, system failures and fraud are all cases of anomalies.  

In this webinar we will:

  • Discuss different types of anomalie
  • Describe machine learning methods for anomaly detection
  • Describe uses cases in which these methods can be applied
About Our Expert


Jesús Calderón has over ten years of experience in internal audit, risk management, and financial controls. He specializes in the application of data science and machine learning to auditing and risk management, including fraud investigations and fraud risk assessments. Jesús has advised Canadian and international clients in the financial services and energy industries on the implementation of data-driven solutions for fraud controls in mortgage operations, wealth management businesses, and capital markets, as well as anti-money laundering.

Continued Risk Learning Credits: 1

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5/29/2019 10:00 AM - 11:00 AM
Thought Leadership Webinar

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