Abstract: In a described embodiment, a first model for processing a first set of data corresponding to a first data space, in which the first model includes a first feature extractor configured to extract a first set of feature representations is implemented. A second model is implemented for processing a second set of data corresponding to a second data space, in which the second model includes a second feature extractor configured to extract a second set of feature representations. Information from the second model to the first model is transferred via a connection, in which the connection links the first model and the second model. The second model is trained using a labeled set derived from the second set of data and the first model is trained using a labeled set derived from the first set of data and a plurality of outputs from the second feature extractor. Parameters of the second model are aggregated to form a global model.

Abstract: Transforming hedging information and Forced Sale Value (FSV) information to a special LC and to a reduction in an applicant's booked credit limit and a reduced risk weighted asset (RWA), along with eliminating or reducing price risk and enabling a financial institution to determine its potential loss upon default, is disclosed. The disclosed LC is obtained through application, in relation to shipment of a commodity and the commodity is hedged against price risk by the issuing financial institution. The issuing financial institution also determines a FSV for the commodity. Thus, the issuing financial institution determines what the commodity would sell for, regardless of price movements, should the buyer not follow through with payment and a forced sale becomes necessary.