Abstract: Computing devices, methods, systems, and computer-readable media for analyzing requests to perform cold restores of cryptocurrencies are described herein. A computing device may receive a request for a cold restore of one or more cryptocurrencies stored by a digital wallet. The one or more cryptocurrencies may be identified, and data may be received from a database. The computing device may determine, based on the data, a risk score associated with the transfer of the one or more cryptocurrencies from a cold state to a hot state. The risk score may be generated using a machine learning model, such as a machine learning model that may be trained to output a risk score associated with a cold restore based on recent transaction activity. The computing device may output, based on comparing the risk score to a threshold, an indication of whether the request should be granted.

Abstract: Computing devices, methods, systems, and computer-readable media for analyzing requests to perform cold restores of cryptocurrencies are described herein. A computing device may receive a request for a cold restore of one or more cryptocurrencies stored by a digital wallet. The one or more cryptocurrencies may be identified, and data may be received from a database. The computing device may determine, based on the data, a risk score associated with the transfer of the one or more cryptocurrencies from a cold state to a hot state. The risk score may be generated using a machine learning model, such as a machine learning model that may be trained to output a risk score associated with a cold restore based on recent transaction activity. The computing device may output, based on comparing the risk score to a threshold, an indication of whether the request should be granted.

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.

Abstract: A method and system for authoring and publishing a future credit policy to a credit decisioning service. A future credit policy is generated in a statistical analysis formant and transformed into an executable code enhanced for credit decisioning. The future credit policy may be tested before deployment using credit decisioning service requests sent to the credit decisioning service using a current credit policy.