Why Data Enrichment and Feature Engineering Make the Difference for Predictive Modeling Quality
A Guide to Fintechs for Improving AI with a Data-Centric Focus
The AI market is booming, and data scientists in the fintech industry who have advanced the craft of combining data and code to solve complex, important problems must be given substantial credit, as do line-of-business leaders who have wisely chosen to invest in a growing method of delivering predictive analytics.
However, the majority of research and attention related to AI concentrates on complicated machine learning techniques and refining algorithm code. It’s imperative to remember that the data used to train algorithms can be much more impactful to predictive modeling accuracy than the machine learning technique used to build the model.
Complete the form to download the guide that explains:
- How data quality, breadth, and depth are crucial to building accurate predictive models
- How data enrichment and feature engineering benefit AI in Fintech
- Why the data-centric AI philosophy is the way forward for the expanding world of AI
If you’re looking for new ways to scale up your firm’s internal database, enhance machine learning, and give your organization a competitive edge, this whitepaper is for you. Please fill out the form for access.