Financial industry has adopted statistical analysis for different tasks for a long time and have accumulated tremendous valuable data. These conditions leave a big potential of AI technologies to empower financial industry.
In particular, we start with the intelligent quant investment as our first exploration area. Now we also expand our research on RegTech like anti-money laundry.
We mainly focus on several typical challenges / research directions in applying AI techniques into Machine learning. 1) How to mine patterns in heterogeneous, noisy and correlated data? 2) How to deal with the data/concept drifting? 3) How to measure and control the risk in a data-driven way? 4) How to model the real-world feedback of a decision and how to coordinate multiple correlated sequential decisions?
We build an opensource AI-oriented quant investment platform Qlib to accelerate the research exploration and algorithm landing.
By solving these common challenges in applying AI technologies in financial industry, we have capabilities to empower companies and customers in financial industry by building Azure services specific for financial industry.
People
Weiqing Liu
Principal Research Manager