Abstract: A system and computer-implemented method for optimising, by a quantum computer, an allocation of opportunities to recipients comprising: determining a plurality of opportunities to be allocated; determining a plurality of recipients to be allocated at least one of the plurality of opportunities; determining a respective acceptance likelihood of each of the plurality of recipients accepting each of the plurality of opportunities; determining a first constraint associated with a cost acceptance of each of the plurality of opportunities by the plurality of recipients; and determining an optimised allocation of the opportunities to the recipients based on the respective likelihoods and the first constraint, in the quantum computer.

Abstract: An apparatus for determining a level of risk that a future transfer will exceed a level of reserve is provided by the present disclosure, the apparatus comprising circuitry configured to: obtain data of transfers between financial institutions which have occurred at a number of instances of time, the data including transfer amounts; apply a predictive model to the data to obtain a prediction of the transfer amount at each instance of time; determine a maximum residual between the prediction of the transfer amount and the transfer amount at each instance of time; model the maximum residual for each instance of time using a generalised extreme value distribution to obtain a distribution function; and determine the level of risk that a future transfer between financial institutions will exceed the level of reserve based on the distribution function which has been obtained.

Abstract: A computer-implemented method for testing a model indicating a parametric relationship between a plurality of channels of multivariate data, the method comprising: obtaining a real dataset of observed multivariate data comprising the plurality of channels; generating a control dataset of multivariate data comprising the plurality of channels, the control dataset being generated based on the model; for each of a plurality of sample subsets from the real dataset and the control dataset, calculating a p-value for the model; and determining whether (1) a distribution difference characteristic of a distribution of the obtained p-values for sample subsets of the real dataset and a distribution of the obtained p-values for sample subsets of the control dataset falls within a second predetermined significance range, and, if condition (1) is met, determining that the model is accurate.

Abstract: An aspect of the present disclosure is drawn to a method for managing a payment network, including: learning a transaction pattern of an account over time; generating a transfer function based on the transaction pattern; predicting information for the account based on the density function; changing a state of the account based on the predicted information; and automatically transmitting a notification of a feature to an owner of the account based on the changing of the state of the account, wherein the transfer function is a probability density function of a neural network and wherein the information includes a time of a future transaction linked to a correlated marker of the payment network.