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In today’s complicated financial landscape predictive analytics are a necessity for banks to remain competitive, but working with data and developing accurate predictive models is challenging. The quality of predictive output relies on the quality of input. That’s why proper data preparation is such a critical success factor for achieving optimal machine learning results. However, getting the data prepared for analysis is a time-consuming process. In addition, models are inherently complex — and if developed poorly can do more harm than good.

Watch this recorded webinar to learn how banks can use Automated Data Preparation & Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements. We discussed current trends and expectations for model risk management regulatory compliance, how to reduce the time it takes to prepare data, and how industry-leading financial institutions are leveraging Machine Learning to provide a much stronger framework for model development and validation than traditional manual efforts.

You’ll discover:

  • How Self-Service Data Preparation reduces the work required to get data ready for predictive modeling
  • Efficient methods to organize complex data and marry multiple datasets for predictive modeling
  • How Automated Machine Learning reduces model risk, while ensuring the implementation of cutting edge machine learning models
  • How Automated Machine Learning enhances compliance to model risk management regulation

Speakers

  •  Seph Mard, Head of Model Risk Management, DataRobot
  • Christopher Moore, Lead Solution Engineer & Data Wrangler, Trifacta

""Trifacta brought an entirely new level of productivity to the way our analyst and IT teams explore diverse data and define analytic requirements. Our users can intuitively and collaboratively prepare the growing variety of data that makes up PepsiCo’s analytic initiatives.""

""We were actually able to shave the amount of time it took to do the analysis by [a factor of] six. Rather than having to do a tremendous amount of analysis, we’re actually readily able to start getting incremental data products out quickly.""