Systems and methods for detecting malware based on anomalous cross-customer financial transactions

Abstract

The disclosed computer-implemented method for detecting malware based on anomalous cross-customer financial transactions may include (i) detecting, using a machine-learning algorithm, a set of anomalies associated with fraudulent financial transactions for source user accounts in a group of customer financial accounts, (ii) identifying, based on customer transaction metadata associated with a group of target user accounts in the customer financial accounts, a cluster of financial transactions having anomaly instances in common with the set of anomalies, (iii) linking each of the customer financial accounts having the common anomaly instances in the cluster of financial transactions with a corresponding customer threat protection account to discover a user device identification, (iv) determining that artifacts appearing on a group of user devices are associated with a potential malware attack, and (v) performing a security action that protects against the potential malware attack. Various other methods, systems, and computer-readable media are also disclosed.