Abstract: Systems and methods generate a risk score for an account event. The systems and methods automatically generate a causal model corresponding to a user, wherein the model estimates components of the causal model using event parameters of a previous event undertaken by the user in an account of the user. The systems and methods predict expected behavior of the user during a next event in the account using the causal model. Predicting the expected behavior of the user includes generating expected event parameters of the next event. The systems and methods use a predictive fraud model to generate fraud event parameters. Generation of the fraud event parameters assumes a fraudster is conducting the next event, wherein the fraudster is any person other than the user. The systems and methods generate a risk score of the next event to indicate the relative likelihood the future event is performed by the user.

Abstract: A computerized-method for initiating a sandbox-testing-process-flow associated with a client-entity, within a server runtime environment and configuring said sandbox testing process flow with money laundering-detection-rules is provided herein.

Abstract: The invention provides methods, systems and computer program products for sandbox enabled testing of money laundering detection rules or rulesets.

Abstract: Systems and methods generate a risk score for an account event. The systems and methods automatically generate a causal model corresponding to a user, wherein the model estimates components of the causal model using event parameters of a previous event undertaken by the user in an account of the user. The systems and methods predict expected behavior of the user during a next event in the account using the causal model. Predicting the expected behavior of the user includes generating expected event parameters of the next event. The systems and methods use a predictive fraud model to generate fraud event parameters. Generation of the fraud event parameters assumes a fraudster is conducting the next event, wherein the fraudster is any person other than the user. The systems and methods generate a risk score of the next event to indicate the relative likelihood the future event is performed by the user.

Abstract: A system is provided comprising a risk engine coupled to a financial system that includes an account. The risk engine generates an account model corresponding to a user and events of the account. Generation of the account model uses event parameters of a previous event performed by the user in the account. The risk engine uses the account model to generate a first probability of observing event parameters assuming the user is conducting the next event. The risk engine uses a fraud model to generate a second probability of observing event parameters assuming a fraudster is conducting the next event. The risk engine generates a risk score, using the first and second probabilities, which indicates the relative likelihood the next event is performed by the user. The system includes a risk application comprising an analytical user interface that displays for any event the risk score and/or event parameters.

Abstract: Systems and methods generate a risk score for an account event. The systems and methods automatically generate a causal model corresponding to a user, wherein the model estimates components of the causal model using event parameters of a previous event undertaken by the user in an account of the user. The systems and methods predict expected behavior of the user during a next event in the account using the causal model. Predicting the expected behavior of the user includes generating expected event parameters of the next event. The systems and methods use a predictive fraud model to generate fraud event parameters. Generation of the fraud event parameters assumes a fraudster is conducting the next event, wherein the fraudster is any person other than the user. The systems and methods generate a risk score of the next event to indicate the relative likelihood the future event is performed by the user.

Abstract: Systems and methods comprise a platform including a processor coupled to a database. Risk engines are coupled to the platform and receive event data and risk data from data sources. The event data comprises data of actions taken in a target account during electronic access of the account, and the risk data comprises data of actions taken in a accounts different from the target account. The risk engines, using the event data and the risk data, dynamically generate an account model that corresponds to the target account, and use the account model to generate a risk score. The risk score represents a relative likelihood an action taken in the target account is fraud. A risk application coupled to the platform includes an analytical user interface that displays for the actions in the target account at least one of the risk score and event data of any event in the account.

Abstract: Systems and methods are provided for predicting expected behavior of a user in an account. The systems and methods automatically generate a causal model corresponding to a user. The systems and methods estimate a plurality of components of the causal model using event parameters of a first set of events undertaken by the user in an account of the user. The systems and methods predict expected behavior of the user during a second set of events using the causal model.

Abstract: Systems and methods generate a risk score for an account event. The systems and methods automatically generate a causal model corresponding to a user, wherein the model estimates components of the causal model using event parameters of a previous event undertaken by the user in an account of the user. The systems and methods predict expected behavior of the user during a next event in the account using the causal model. Predicting the expected behavior of the user includes generating expected event parameters of the next event. The systems and methods use a predictive fraud model to generate fraud event parameters. Generation of the fraud event parameters assumes a fraudster is conducting the next event, wherein the fraudster is any person other than the user. The systems and methods generate a risk score of the next event to indicate the relative likelihood the future event is performed by the user.

Abstract: Systems and methods generate a risk score for an account event. The systems and methods automatically generate a causal model corresponding to a user, wherein the model estimates components of the causal model using event parameters of a previous event undertaken by the user in an account of the user. The systems and methods predict expected behavior of the user during a next event in the account using the causal model. Predicting the expected behavior of the user includes generating expected event parameters of the next event. The systems and methods use a predictive fraud model to generate fraud event parameters. Generation of the fraud event parameters assumes a fraudster is conducting the next event, wherein the fraudster is any person other than the user. The systems and methods generate a risk score of the next event to indicate the relative likelihood the future event is performed by the user.