Credit scoring is evolving beyond traditional tabular data. By incorporating multiple data modalities—such as transaction history, text data, and alternative signals—machine learning models can deliver more accurate and nuanced assessments of risk. This hands-on session will show you how to build modern credit scoring systems using multimodal approaches.
In this code-along webinar, María Óskarsdóttir, a Professor at the University of Southampton, will guide you through building and analyzing a credit scoring model in Python. You’ll explore how to combine different types of data, design models that leverage multimodal inputs, and evaluate performance in a real-world financial context. This session is ideal for ML practitioners looking to push beyond standard modeling techniques.
Key Takeaways
- Learn how to build and evaluate credit scoring models using Python and neural networks.
- Understand how multimodal data can improve model performance and insight.
- Apply your skills to a real-world credit scoring use case with hands-on guidance.
