Abstract: Implementations of the present specification provide a data processing method, apparatus, and device. The method includes: obtaining to-be-detected target data, and obtaining a target probability that the target data corresponds to each candidate user intention, where the target data includes input data of a user in a human-computer interaction process; dividing the target data to obtain a plurality of pieces of subdata, and obtaining, based on a predetermined gradient integration algorithm, a contribution of each piece of subdata to a correspondence between the target data and each candidate user intention; and determining a target user intention corresponding to the target data based on the target probability that the target data corresponds to each candidate user intention and the contribution of each piece of subdata to the correspondence between the target data and each candidate user intention.

Abstract: One embodiment of the present disclosure provides a system and method for determining a decision-making strategy. During operation, the system can obtain sample data corresponding to a service. The system can pre-determine, based on the sample data and the service, a basic parametric shape model and can generate a plurality of shape parameters for the basic parametric shape mode. The system can then generate one or more parametric shape models with different shapes based on the plurality of shape parameters. The system can determine goodness-of-fit of the one or more parametric shape models with respect to a pre-defined decision-making strategy. Next, the system can in response to determining that the goodness-of-fit of at least one parametric shape model satisfies a set of objectives, outputting the at least one parametric shape model as an optimum decision-making strategy for the service.

Abstract: Implementations of the present specification disclose an event risk detection method, apparatus, and device.

Abstract: One embodiment provides a method and system for managing risk-control commands. During operation, the system can obtain statistics associated with a plurality of risk-control commands issued by a risk-control system corresponding to a plurality of transactions on an online financial platform, and determine, based on the monitored plurality of risk-control commands, whether a subset of risk-control commands meets an anomaly condition. In response to determining that the subset of risk-control commands does not meet the anomaly condition, the system can forward the subset of risk-control commands to the online financial platform to facilitate the online financial platform in performing corresponding transactions according to the subset of risk-control command. In response to determining that the subset of risk-control commands meets the anomaly condition, the system can prevent the subset of risk-control commands from being forwarded to the online financial platform.

Abstract: A method for identifying a risk pertaining to a transaction to be processed, includes: identifying a risk pertaining to a transaction to be processed via a fast identification layer in a risk control system, thereby obtaining a first identification result; identifying a risk of interference pertaining to the transaction to be processed via a model for identifying transactions susceptible to interference, thereby obtaining a second identification result; and determining a processing manner for the transaction to be processed based on the first identification result and the second identification result, the processing manner including releasing the transaction to be processed, or further identifying the risk pertaining to the transaction to be processed via the in-depth identification layer in the risk control system.

Abstract: A method for identifying a risk pertaining to a transaction to be processed, includes: identifying a risk pertaining to a transaction to be processed via a fast identification layer in a risk control system, thereby obtaining a first identification result; identifying a risk of interference pertaining to the transaction to be processed via a model for identifying transactions susceptible to interference, thereby obtaining a second identification result; and determining a processing manner for the transaction to be processed based on the first identification result and the second identification result, the processing manner including releasing the transaction to be processed, or further identifying the risk pertaining to the transaction to be processed via the in-depth identification layer in the risk control system.

Abstract: A feature-selection system obtains a training data set and associated features; divides the training data set into a first number of training data subsets; and forms a plurality of feature-selecting data sets. A feature-selecting data set comprises a second number of training data subsets. The system processes, in parallel, each feature-selecting data set, which comprises: computing a first evaluation index for the features based on the feature-selecting data set; obtaining a group of index ranks corresponding to the features based on the first evaluation index; and obtaining a group of importance ranks corresponding to the features based on the feature-selecting data set and a machine-learning model. The system further obtains a group of total ranks by fusing groups of index ranks and groups importance ranks obtained from processing the plurality of feature-selecting data sets; and selecting target features from the features based on the group of total ranks.

Abstract: A model integration method and device are provided. The method includes: obtaining an integrated model, the integrated model having one integrated output value and a plurality of input values, the plurality of input values corresponding to a plurality of output values of a plurality of independent models; performing one or more iterations of optimizing process until a preset iteration stop condition is satisfied: acquiring a prediction output by the integrated model based on a preset test event set; determining an index value of the integrated model based on the prediction output, the index value indicates a performance evaluation of the integrated model; if the index value fails to meet a preset performance requirement; after the preset iteration stop condition is satisfied, determining the integrated model as acceptable.

Abstract: One embodiment provides an identification method and system. The system obtains attribute features of a user, determines a first set of identification products and identification success rates thereof based on the attribute features of the user and a first machine-learning model for determining identification security, and generates a first set of identification product combinations, a respective identification product combination comprising two different identification products.

Abstract: One embodiment provides an identification method and system. The system obtains attribute features of a user, determines a first set of identification products and identification success rates thereof based on the attribute features of the user and a first machine-learning model for determining identification security, and generates a first set of identification product combinations, a respective identification product combination comprising two different identification products.

Abstract: A feature-selection system obtains a training data set and associated features; divides the training data set into a first number of training data subsets; and forms a plurality of feature-selecting data sets. A feature-selecting data set comprises a second number of training data subsets. The system processes, in parallel, each feature-selecting data set, which comprises: computing a first evaluation index for the features based on the feature-selecting data set; obtaining a group of index ranks corresponding to the features based on the first evaluation index; and obtaining a group of importance ranks corresponding to the features based on the feature-selecting data set and a machine-learning model. The system further obtains a group of total ranks by fusing groups of index ranks and groups importance ranks obtained from processing the plurality of feature-selecting data sets; and selecting target features from the features based on the group of total ranks.

Abstract: A method for determining critical values includes obtaining N relation functions between M features and N index values, the N relation functions taking the M features as inputs and the N index values as outputs; generating K combinations, each comprising M critical values of the M features; repeating following steps until a preset stop condition is satisfied: determining a score for each of the K combinations based on the N relation functions to represent an overall quality of the N index values; repeatedly selecting two combinations and exchanging critical values between the two combinations to generate two new combinations until a quantity of the new combinations reaches a preset number; and selecting K combinations from the new combinations; and after the preset stop condition is satisfied, associating the M features with the M critical values in a combination with a highest score.

Abstract: One embodiment of the present disclosure provides a system and method for determining a decision-making strategy. During operation, the system can obtain sample data corresponding to a service. The system can pre-determine, based on the sample data and the service, a basic parametric shape model and can generate a plurality of shape parameters for the basic parametric shape mode. The system can then generate one or more parametric shape models with different shapes based on the plurality of shape parameters. The system can determine goodness-of-fit of the one or more parametric shape models with respect to a pre-defined decision-making strategy. Next, the system can in response to determining that the goodness-of-fit of at least one parametric shape model satisfies a set of objectives, outputting the at least one parametric shape model as an optimum decision-making strategy for the service.

Abstract: One embodiment provides a method and system for managing risk-control commands. During operation, the system can obtain statistics associated with a plurality of risk-control commands issued by a risk-control system corresponding to a plurality of transactions on an online financial platform, and determine, based on the monitored plurality of risk-control commands, whether a subset of risk-control commands meets an anomaly condition. In response to determining that the subset of risk-control commands does not meet the anomaly condition, the system can forward the subset of risk-control commands to the online financial platform to facilitate the online financial platform in performing corresponding transactions according to the subset of risk-control command. In response to determining that the subset of risk-control commands meets the anomaly condition, the system can prevent the subset of risk-control commands from being forwarded to the online financial platform.

Abstract: A model integration method and device are provided. The method includes: obtaining an integrated model, the integrated model having one integrated output value and a plurality of input values, the plurality of input values corresponding to a plurality of output values of a plurality of independent models; performing one or more iterations of optimizing process until a preset iteration stop condition is satisfied: acquiring a prediction output by the integrated model based on a preset test event set; determining an index value of the integrated model based on the prediction output, the index value indicates a performance evaluation of the integrated model; if the index value fails to meet a preset performance requirement; after the preset iteration stop condition is satisfied, determining the integrated model as acceptable.

Abstract: A method for determining critical values includes obtaining N relation functions between M features and N index values, the N relation functions taking the M features as inputs and the N index values as outputs; generating K combinations, each comprising M critical values of the M features; repeating following steps until a preset stop condition is satisfied: determining a score for each of the K combinations based on the N relation functions to represent an overall quality of the N index values; repeatedly selecting two combinations and exchanging critical values between the two combinations to generate two new combinations until a quantity of the new combinations reaches a preset number; and selecting K combinations from the new combinations; and after the preset stop condition is satisfied, associating the M features with the M critical values in a combination with a highest score.

Abstract: A method for identifying a risk pertaining to a transaction to be processed, includes: identifying a risk pertaining to a transaction to be processed via a fast identification layer in a risk control system, thereby obtaining a first identification result; identifying a risk of interference pertaining to the transaction to be processed via a model for identifying transactions susceptible to interference, thereby obtaining a second identification result; and determining a processing manner for the transaction to be processed based on the first identification result and the second identification result, the processing manner including releasing the transaction to be processed, or further identifying the risk pertaining to the transaction to be processed via the in-depth identification layer in the risk control system.

Abstract: A method for identifying a risk pertaining to a transaction to be processed, includes: identifying a risk pertaining to a transaction to be processed via a fast identification layer in a risk control system, thereby obtaining a first identification result; identifying a risk of interference pertaining to the transaction to be processed via a model for identifying transactions susceptible to interference, thereby obtaining a second identification result; and determining a processing manner for the transaction to be processed based on the first identification result and the second identification result, the processing manner including releasing the transaction to be processed, or further identifying the risk pertaining to the transaction to be processed via the in-depth identification layer in the risk control system.

Abstract: A system and method are used to identify a targeted consumer. The system includes a partial social security number (SSN) system that identifies a partial SSN issued during a preselected time period and a marketing database system. The marketing database system receives a comparison result from a SSN matching system that identifies at least one individual in a consumer database having a partial SSN that matches the partial SSN identified by the partial SSN system. The marketing database system places identifying information of the at least one individual in a marketing database. In one method, an individual is identified as a targeted consumer based on the individual's age being within a preselected age range and the individual's social security number being issued within a predetermined social security number SSN issue date range. Identifying information of the targeted consumer is stored in a contact database, which a business may use to directly market its goods or services.