Abstract: According to various embodiments, the integration of automated agents adds convenience and reduces time requirements on individuals, but also significantly magnifies security risks, including, for example, security risks in cases of fraud (e.g., unauthorized use of agents to purchase goods, agents masquerading as authorized, etc.). According to various embodiments, the transacting entity (the agent) can be supported by a security infrastructure that ensures the agent carries the reputation of the entity they are operating on behalf of, and provides immutable constraints on their operation. For example, the agent can operate based on the strength of authentication of its connection to the individual. In some embodiments, the security architecture can include delegation for chains of payment permissions, where agents create new sub-agents. The strength of the reputation required may vary by the type of agent and desired action, among other factors.

Abstract: Verification systems and techniques are described. Verification systems may include at least one processor operatively connected to a memory configured to determine a probabilistic linkage, in real time, between a user initiating an event at a merchant and user identities associated with previous events through analysis executed on at least one of the user's attributes or attributes of the user identity; identify, based on the probabilistic link, a user identity of the user identities corresponding to the user initiating the event; and enhance authorization or authentication decisioning for the event in response to the probabilistic linkage to at least one of a bad or good actor persona, the authorization or authentication decisioning optionally including preventing the event in response to determining the user identity is associated with the bad actor persona or permitting the event in response to determining the user identity is associated with the good actor persona.

Abstract: According to one aspect, systems and methods are provided that intelligently automate payment routing. According to various embodiments, the intelligent routing can be augmented with machine learning models that generate an optimal routing of a transaction, wherein for example, the optimal routing can be based on a unique set of circumstances for each transaction. In further embodiments, machine learning models accept payment processing information and circumstance (e.g., payment modality, store location, online payment location, source IP address, payor IP address, merchant IP address, physical presence, intermediary information, merchant information, payment brand, supplier data, among a host of other options) and based on the trained model can generate an optimal path for any particular transaction.