In today’s complicated financial landscape, predictive analytics capabilities are necessary 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.
Register for this webinar to learn how banks can use automated data preparation and machine learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements. We will discuss 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.
- 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