From Raw Transactions to Insights: The Power of Embeddings in Fintech

Financial transactions generate vast amounts of sequential data, yet traditional risk assessment models often rely on predefined features that may not capture the full complexity of user behavior. This talk explores how transaction embeddings—inspired by techniques from NLP and Computer Vision—can transform financial modeling.

Basic knowledge of ML and DL

In financial services, transactional data holds valuable insights, yet traditional models often rely on feature engineering, which can be time-consuming and restrictive. This talk explores how transaction embeddings can capture richer representations of financial behavior, leading to more accurate and scalable models. We’ll discuss how these embeddings are generated, the techniques used to learn meaningful representations, and their integration into machine learning models to enhance predictive performance. Attendees will gain practical insights into leveraging embeddings in fintech applications, with a focus on improving risk estimation and decision-making.