Systems and methods for risk diagnosis of cryptocurrency addresses on blockchains using anonymous and public information

Abstract

Systems and methods for risk diagnosis of cryptocurrency addresses on blockchains using anonymous and public information are disclosed. In one embodiment, a method may include a risk diagnosis computer program executed by a server: receiving data with labels and data without labels from public data databases and/or anonymous data databases; fitting the data without labels into a plurality of clusters using unsupervised learning; assigning a risk level to each of the plurality of clusters; verifying the risk level for each of the plurality of clusters using the data with labels; receiving unseen data; and predicting a risk level associated with the unseen data by constructing features and text embeddings from the unseen data, clustering the unseen data based on a distance measurement to one of the plurality of clusters, and returning a risk level associated with the cluster.