Abstract. It is expected that by 2023. 30% of the organizations worldwide will already be using graph technologies to facilitate rapid contextualization for decision making, and that application of graph processing and graph databases will grow at 100% annually according to Gartner. Graphs offer a novel source of information since they accurately and adequately capture the interactions of different entities of interest such as organizations, people. devices, and transactions. In fact, it has been discovered that connections in data are as valuable as the data itself, as these provide context allowing algorithms to learn not only from the datapoint itself but also from the structure created and the flow of information. The interest In these technologies and algorithms have mad : the field of graph machine learning the fastest growing field in the major Al conferences. In this talk, we will briefly describe the concepts needed to understand Graph Machine Learning, describe the evolution it has taken in its methodologies, give a brief overview of the field, and finally. show real use cases of graph machine learning algorithms to detect fraudulent activities, identify potential influencers. and to enhance credit risk scores. La presentación se hará en español.

Alejandro Correa Bahnsen. Chief Artificial Intelligence Officer at RappiBank. With a passion for machine learning, he considers himself a technology evangelist of data science. He has more than a decade of experience applying the use and development of Artificial Intelligence to real-world issues such as cybersecurity, risk management, and marketing. Alejandro is also an Adjunct Professor in the Industrial Engineering department of Universidad de Los Andes in Bogotá, where he teaches Deep Learning and Applied Data Science classes for the undergraduate and masters programs. He holds a PhD in Machine Learning and Pattern Recognition from Luxembourg University. https://www.linkedin.com/in/albahnsen/

Luisa Roa. Data Scientist at RappiBank, graduated from the Universidad de los Andes in Industrial Engineering and is currently finishing her Master’s Degree in Operations Research and Statistics. His work has focused on investigating the value of alternative data and graphs for fraud and credit risk issues.

Jaime Acevedo. Data Scientist at RappiBank focused on applied research. B.S. in Industrial Engineering currently pursuing an M.S. in Operations and Finance Research with specialized studies in Machine Learning models, deterministic and stochastic optimization, financial engineering and risk management. It also has complementary studies on effective communication methods.