Advanced New Technologies Co., Ltd.

Hong Jin

Wenbiao Zhao

Weiqiang Wang

Dapeng Fu

Chao Li

Hua Zhao

Lijie Zhang

Yuzhou Tang

Chunjie Dong

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.

System and method for determining a decision-making strategy

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for payment services are provided. One of the methods includes: receiving a payment request, the payment request containing a user identifier and operation data generated in a current transaction corresponding to the user identifier; determining, according to the operation data, at least one operation hopping sequence in the current transaction, the operation hopping sequence containing at least one operation hopping event; obtaining, from a set of risk parameters corresponding to the user identifier, a risk parameter corresponding to the operation hopping event in the operation hopping sequence, risk parameters in the set of risk parameters being obtained by adjusting risk parameters used for risk identification in a previous transaction according to operation data generated in the previous transaction corresponding to the user identifier; and adjusting the obtained risk parameter according to the at least one operation hopping sequence.

Method for adjusting risk parameter, and method and device for risk identification

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.

Feature selection method, device and apparatus for constructing machine learning model

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. The system selects, from the first set of identification product combinations, a second set of identification product combinations based on the identification success rates of the first set of identification products; and selects, from the second set of identification product combinations, a third set of identification product combinations based on the attribute features of the user and a second machine-learning model for determining identification applicability, thereby facilitating identification of the user using at least one of the third set of the identification product combinations.

Identification method and apparatus

A computer-implemented method includes: receiving, by a computing device, a text report request from a user device associated with a user; obtaining a behavior history and personal information of the user; inputting the behavior history and the personal information of the user into a model, to obtain a plurality of personalized evaluation results, each personalized evaluation result corresponding to a respective text report category of a plurality of text report categories, in which each personalized evaluation result indicates a predicted relevance of the corresponding text report category to a problem faced by the user, and in which the model includes a classification model trained using one or more supervised learning techniques on a plurality of user behavior history samples and a plurality of personal information samples; and determining an order in which the plurality of text report categories are to be presented to the user.

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Hangzhou, China

Hong Jin