Abstract: Techniques are described for predicting the likelihood that a loan default will occur. The technique can be performed pro-actively, in order to predict situations in which a loan default is likely even before any payment has been missed on the loan. Upon detecting a high likelihood of default, the loan default prediction system may automatically execute remedial actions. For example, the loan default prediction system may automatically generate an offer, to the borrower in question, to allow the borrower to skip the next loan payment. The technique may also be used to generate accurate financial health scores that take into account trends in a borrower's activities. The actions that are automatically performed based on the financial health scores may include both remedial actions and reward actions. The outcomes of the actions may be fed back into the system to further refine the model used thereby.

Abstract: Embodiments allow a mock-enabled software module to access a store of mock output data. Sets of mock output values (“SMOVs”), stored within the mock output store, are mapped to one or more key input values. When input sent to a mock-enabled software module includes one or more key input values that map to a given SMOV, the SMOV is included in a mock response from a target module of the given SMOV. When a set of input values, sent to a mock-enabled software module, does not include key input values that map to a SMOV, the mock-enabled module produces output without triggering any mock response. The mock output store may contain one or more replacement templates that are used to replace one or more mock output values, in a mock response, with one or more corresponding input values from the set of input values received by the triggering module.