10 Patterns of Centralized Crypto Exchanges Explained Using Machine Learning and Data Visualizations

At IntoTheBlock, we have been working on a series of machine learning models that help us better understand the internal behavior of centralized crypto exchanges. Anonymity, non-standard blockchain reconciliation procedures and regular wash trading behaviors are some of the factors that challenge most analyses of centralized exchanges. Ensemble Learning can be used to combine multiple models into a single knowledge structure that can understand internal patterns of centralized crypto exchanges, such as deposit or withdrawal. Data visualizations have proven to be a unique asset to understand the behavior of the exchanges.