Abstract: Systems and methods for assessing a credit exposure risk are provided. The methods include collecting credit facility data from at least one credit lender server device; determining, based on the credit facility data, at least one feature vector that is associated with at least one credit borrower device; applying at least one escalation rule to the feature vector; determining whether the at least one feature vector satisfies the applied escalation rule; and when the at least one feature vector is determined as not satisfying the applied escalation rule, performing an escalation action with respect to an entity associated with the credit borrower device.

Abstract: Systems and methods for privacy-preserving enablement of connections within organizations are disclosed. In one embodiment, a method may include (1) receiving, at a server comprising a computer processor, an identification of a target to contact from a requester in an organization; (2) the computer processor identifying, in a connection database, at least one user within the organization having a connection with the target; (3) the computer processor communicating anonymized information representing the at least one user having the connection and a relationship-strength score for the connection; (4) the computer processor communicating a request for introduction assistance to the at least one user; (5) the computer processor receiving acceptance of the request for introductory assistance from the at least one user; and (6) the computer processor identifying the at least one that accepted the request to the requester.

Abstract: Methods and systems for managing data analytics are provided. The methods and systems entail obtaining a configuration file that includes steps. Each step includes at least one parameter that identifies a source of input data, a transformation that corresponds to business logic to be applied to the input data, and/or a sink of output data resulting from a transformation to the input data. A directed acyclic graph is generated based on the configuration file. The directed acyclic graph is then executed by invoking an application programming interface (API) of a data transformation engine to apply the transformations to the input data, and then returning an indication of completion to a source of the configuration file.

Abstract: Systems and methods for hierarchical dual-dynamic exception management are disclosed. In one embodiment, the system and method may provide: (1) hierarchical exception modelling, metric algorithms and aggregation; (2) node level exception metric algorithm and high-level status algorithm calculation; (3) node level investigation via “explain” of exception factors (e.g., focusing an analyst on a particular issue); (4) node level tracking workflow management through ability to add commentary, multiple-person signoff, and high-level status calculation overrides; (5) visibility, auditable compliance reporting; (6) dashboard for internal operational analysts/managers with full investigation view; (7) dashboard for external clients with sub views; (8) generic data interface to existing workflow management system; and (9) machine learning component such that the exception metric can be intelligently re-adjusted (for example, if there are too many false positives then make the thresholds more forgiving).

Abstract: An embodiment of the present invention provides computer-implemented methods and systems for optimizing the executing an order, such as trading orders. An order may be electronically routed to an Execution Optimizer (“EO”). The EO may apply a particular profile to the order, corresponding to a particular portfolio manager. Next, the order, with the profile, may be routed, electronically, to a third party where a prediction model may be applied to the order, indicating trading parameters for the order. The order, with the trading parameters from the prediction model, may be passed back to the EO, where a rules engine may apply rules, specific to the executing financial institution, to the order. The order may then be passed to a selected broker for market trading.

Abstract: An embodiment of the present invention provides computer-implemented methods and systems for optimizing the executing an order, such as trading orders. An order may be electronically routed to an Execution Optimizer (“EO”). The EO may apply a particular profile to the order, corresponding to a particular portfolio manager. Next, the order, with the profile, may be routed, electronically, to a third party where a prediction model may be applied to the order, indicating trading parameters for the order. The order, with the trading parameters from the prediction model, may be passed back to the EO, where a rules engine may apply rules, specific to the executing financial institution, to the order. The order may then be passed to a selected broker for market trading.