César Soto Valero
César is currently a Data Scientist at SEB Group, where he develops AI models to enhance the security of financial transactions on a global scale. He completed an M.Sc. in Machine Learning and moved to Sweden in 2018 to pursue a Ph.D. in Computer Science at KTH Royal Institute of Technology. During his five years at KTH, he pioneered open-source tools and techniques to mitigate software bloat, contributing to the efficiency and security of modern software systems. César is deeply passionate about AI, science, and technology, with a strong focus on bridging cutting-edge research with real-world applications. He is dedicated to advancing AI’s role in building smarter, more resilient systems that drive innovation.
Sessions
Building ML models for financial fraud detection sounds straightforward, until you have to evaluate, validate, and deploy them in real-world pipelines. This talk walks through the practical stack, metrics, and mindsets needed to build fraud detection systems with modern Python. We'll cover key challenges like concept drift, extreme class imbalance, false-positive overload, and why the usual ML workflows fall short. Along the way, we’ll explore a real-world architecture using classical ML, deep learning, and GNNs, plus the validation techniques and production patterns that make or break fraud systems. If you're tired of toy problems and want patterns that survive real money and real latency, this talk’s for you.
The modern Python ecosystem shortens the distance between idea and implementation. This talk presents a focused workflow to move from a business question to a working prototype, fast. We'll explore reproducible environments (uv, Docker), quick data iteration with polars and duckdb, clean project scaffolding (pyproject.toml), and lightweight service layers with FastAPI and pydantic. Along the way, we’ll integrate tests (pytest), static checks (mypy), and fast linting (ruff). You’ll leave with a reusable structure, toolchain recommendations, and a mental model for optimizing feedback loops and development in modern Python projects.