Abstract: A method for using a Gaussian Process-based algorithm to approximate an optimal stopping of a time series that corresponds to a sequence of events is provided. The method includes: receiving information that relates to an event sequence; estimating, based on the received information, a first potential reward that is obtained by stopping the event sequence at a first time, and a set of respective second potential rewards that are obtained by stopping the event sequence at corresponding times; and determining, based on the estimated first and second potential rewards, an optimal time for stopping the event sequence. The event sequence may include a numerical sequence that is modeled as a statistical learning method via a Gaussian Process (GP) function and/or a deep GP function that indicates a probability density distribution of the items in the numerical sequence over a predetermined time interval.

Abstract: A method and a system for generating a counterfactual explanation for an artificial intelligence (AI) regression model are provided. The method includes: obtaining a mathematical expression that corresponds to the AI regression model and a first value that corresponds to a query instance; and defining a counterfactual potential function by performing a differential continuous mapping between respective output values of the obtained mathematical expression and a real line over a predetermined subset of real numbers. The counterfactual explanation is generated based on a value of the obtained mathematical expression that corresponds to a maximum value of the determined at least one counterfactual potential function.

Abstract: A method and a system for performing stochastic sequential assignment of jobs with random arrival times is provided. The method includes receiving a first plurality of jobs in a sequence; and sequentially applying, to each respective job from among the first plurality of jobs, a non-parametric sequential allocation algorithm in order to determine whether to accept the respective job or to decline the respective job. The application of the non-parametric sequential allocation algorithm includes calculating, for each respective job, a corresponding reward value that relates to a reward that is gained when the respective job is accepted; and maximizing an expected cumulative reward value based on the calculated reward values.

Abstract: Systems and methods for multi-agent based fraud detection are disclosed. A method may include: providing a generator configuration file comprising a plurality of transaction behaviors and a number or proportion of generator agents to act in accordance with each transaction behavior; providing a detector configuration file comprising a detector parameter for a plurality of detector agents to use; generating a first set of test data using the generator agents based on the transaction behavior, wherein the first set of generated test data may include a first set of generated test transactions, and each generated test transactions may include a fraud indicator based on the transaction behavior; training a plurality of detector agents using the first set of generated test data and the detector configuration file, wherein each detector agent outputs a first trained model object; and outputting a first trained detection model based on the first trained model objects.

Abstract: Systems and methods for applying game theory for fraud detection. Rather than inspecting every transaction record, embodiments are directed to limiting incoming suspicious transaction records according to a schedule. The schedule may define time windows for various clients and transactions. These time windows may filter down the stream of incoming transaction records to a subset. As a result, fraud may be detected by strategically allocating resources in an optimal way rather than attempting to inspect each and every instance of transaction.