Abstract: A method, system, and computer program product is provided for graph-based fraud detection. The system includes at least one processor programmed or configured to generate a graph data structure based on a plurality of transactions between a plurality of accounts, wherein each account of the plurality of accounts is represented by a node in the graph data structure, and wherein each transaction of the plurality of transactions is represented by an edge in the graph data structure, determine a plurality of features of the graph data structure for each account of the plurality of accounts, generate a graph profile for at least one account of the plurality of accounts based on the plurality of features for the at least one account, and update the graph profile for the at least one account based on at least one new transaction engaged in by the at least one account.

Abstract: Methods, systems, and computer program products for community detection: (i) obtain a plurality of node embeddings associated with a graph; (ii) determine a number of clusters into which the plurality of node embeddings is to be clustered; (iii) cluster, based on distances between pairs of node embeddings, the plurality of node embeddings into the number of clusters until, for each node embedding in each cluster, a node associated with that node embedding is within k-hops in the graph of each other node associated with each other node embedding in that cluster; (iv) reposition centroids of the number of clusters; (v) repeat steps (iii) and (iv) until a first stopping criteria is satisfied; (vi) repeat steps (ii) through (v) until a second stopping criteria that depends on a conductance of a clustering including the number of clusters is satisfied; and (vii) provide the clustering including the number of clusters.

Abstract: Provided are systems that include a processor to receive a training dataset including a plurality of data records, calculate feature projection errors and classification scores for the plurality of data records using a machine learning model, determine a distribution of features according to feature projection error, apply a downscaling function to each feature value of a feature in a false positive classification distribution to provide a downscaled set of feature values, apply an upscaling function to each feature value of a feature in a false negative classification distribution to provide an upscaled set of feature values, combine the downscaled set of feature values and the upscaled set of feature values with the training dataset to provide an updated training dataset, and train the machine learning model using the updated training dataset to provide an updated trained machine learning model. Methods and computer program products are provided.

Abstract: A method for automatically generating a suggested fraud rule for an issuer includes: identifying a first issuer from a plurality of issuers; determining a transaction volume category and a region category associated with the first issuer; determining a second issuer from the plurality of issuers associated with the same transaction volume category and region category; associating the first issuer and the second issuer to form an issuer consortium; obtaining consortium transaction data associated with the issuer consortium; generating a suggested fraud rule for the first issuer based on the consortium transaction data; and communicating the suggested fraud rule to a first issuer system associated with the first issuer.