1. How to Scale AI in Financial Services Institutions (Michael Soucek)

Financial services institutions were data driven long before big data became a buzz-word. So why are many organizations struggling to get full potential of data at core of their business? Our point of view talks on three building blocks successful data driven organizations have in common. First, an operating model allowing to focus on the right processes. Second, technical stack allowing to scale data products. Third, data culture which is different in historically data heavy organizations.

2. Causality in Banking and Financial Use Cases (Maksim Sipos)

Typical machine learning models fit historic data based on correlations. These models fail when environments change (for example when a macroeconomic regime shift occurs). Causal models are more robust and generalize better. However, inferring causality from data is hard. In this talk, we will describe what techniques we use to build causal models and the benefits we have observed in consumer lending and portfolio risk use cases.

3. Real-Time Feedback in Algorithmic Trading (Andreas Petrides)

Algorithmic trading revolves around optimal scheduling, when and how to trade each asset of a portfolio. The schedules produced, however, should not be static; being able to optimally adapt to changing intraday market conditions and analysing effectively other exogenous information is of paramount importance. In this talk, we focus on how machine learning methods can enrich the toolbox available for real-time algorithmic trading.