METHOD, APPARATUS, AND DEVICE FOR TRAINING RISK MANAGEMENT MODELS
Implementations of the present specification disclose a risk control model training and risk control method, apparatus, and device. In one aspect, the method includes obtaining historical data generated during a specified time period, and partitioning the specified time period into multiple sub time periods; for each sub time period, determining respective features of the historical data in the sub time period; generating a plurality of feature sequences, comprising, for each feature type: sorting the respective features of the historical data belonging to the feature type in the multiple sub time periods based on a corresponding sorting rule; and training a risk management machine learning model by using the plurality of feature sequences as training samples.
1 . A computer-implemented method, comprising:
obtaining historical data generated during a specified time period, and partitioning the specified time period into multiple sub time periods;
for each sub time period, determining respective features of the historical data in the sub time period;
generating a plurality of feature sequences, comprising, for each feature type:
sorting the respective features of the historical data belonging to the feature type in the multiple sub time periods based on a corresponding sorting rule; and
training a risk management machine learning model by using the plurality of feature sequences as training samples.
2 . The computer-implemented method according to claim 1 , wherein:
determining respective features of the historical data in the sub time period comprises, for each feature type:
determining respective features of the historical data that belong to the feature type in the multiple sub time periods; and
training the risk management machine learning model by using the plurality of feature sequences as training sample comprises:
training the risk management machine learning model by using the feature sequence corresponding to each feature type as a training sample.
3 . The computer-implemented method according to claim 1 , wherein sorting the respective features of the historical data belonging to the feature type in the multiple sub time periods comprises:
performing normalization processing on the respective features of the historical data belonging to the feature type in the multiple sub time periods.
4 . The computer-implemented method according to claim 2 , wherein the risk management machine learning model is a convolutional neural network model.
5 . The computer-implemented method according to claim 4 , wherein at least one of a height or a width of a convolutional kernel of a convolutional layer in the convolutional neural network model is equal to a quantity of the plurality of feature types.
6 . The computer-implemented method according to claim 1 , further comprising, after training the risk management machine learning model using the training samples:
obtaining service data generated during a specified time period that describes a transaction activity, and partitioning the specified time period into one or more sub time periods;
for each sub time period, determining respective features of the service data in the sub time period;
generating a plurality of feature sequences, comprising, for each feature type:
sorting the respective features of the service data belonging to the feature type in the one or more sub time periods based on a corresponding sorting rule; and
classifying whether the transaction activity described by the service data is legal by inputting the plurality of feature sequences into the risk management machine learning model to generate a classification output.
7 . A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
obtaining historical data generated during a specified time period, and partitioning the specified time period into multiple sub time periods;
for each sub time period, determining respective features of the historical data in the sub time period;
generating a plurality of feature sequences, comprising, for each feature type:
sorting the respective features of the historical data belonging to the feature type in the multiple sub time periods based on a corresponding sorting rule; and
training a risk management machine learning model by using the plurality of feature sequences as training samples.
8 . The non-transitory, computer-readable medium according to claim 7 , wherein:
determining respective features of the historical data in the sub time period comprises, for each feature type:
determining respective features of the historical data that belong to the feature type in the multiple sub time periods; and
training the risk management machine learning model by using the plurality of feature sequences as training sample comprises:
training the risk management machine learning model by using the feature sequence corresponding to each feature type as a training sample.
9 . The non-transitory, computer-readable medium according to claim 7 , wherein sorting the respective features of the historical data belonging to the feature type in the multiple sub time periods comprises:
performing normalization processing on the respective features of the historical data belonging to the feature type in the multiple sub time periods.
10 . The non-transitory, computer-readable medium according to claim 8 , wherein the risk management machine learning model is a convolutional neural network model.
11 . The non-transitory, computer-readable medium according to claim 10 , wherein at least one of a height or a width of a convolutional kernel of a convolutional layer in the convolutional neural network model is equal to a quantity of the plurality of feature types.
12 . The non-transitory, computer-readable medium according to claim 7 , wherein the operations further comprise, after training the risk management machine learning model using the training samples:
obtaining service data generated during a specified time period that describes a transaction activity, and partitioning the specified time period into one or more sub time periods;
for each sub time period, determining respective features of the service data in the sub time period;
generating a plurality of feature sequences, comprising, for each feature type:
sorting the respective features of the service data belonging to the feature type in the one or more sub time periods based on a corresponding sorting rule; and
classifying whether the transaction activity described by the service data is legal by inputting the plurality of feature sequences into the risk management machine learning model to generate a classification output.
13 . A computer-implemented system, comprising:
one or more computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:
obtaining historical data generated during a specified time period, and partitioning the specified time period into multiple sub time periods;
for each sub time period, determining respective features of the historical data in the sub time period;
generating a plurality of feature sequences, comprising, for each feature type:
sorting the respective features of the historical data belonging to the feature type in the multiple sub time periods based on a corresponding sorting rule; and
training a risk management machine learning model by using the plurality of feature sequences as training samples.
14 . The computer-implemented system according to claim 13 , wherein:
determining respective features of the historical data in the sub time period comprises, for each feature type:
determining respective features of the historical data that belong to the feature type in the multiple sub time periods; and
training the risk management machine learning model by using the plurality of feature sequences as training sample comprises:
training the risk management machine learning model by using the feature sequence corresponding to each feature type as a training sample.
15 . The computer-implemented system according to claim 13 , wherein sorting the respective features of the historical data belonging to the feature type in the multiple sub time periods comprises:
performing normalization processing on the respective features of the historical data belonging to the feature type in the multiple sub time periods.
16 . The computer-implemented system according to claim 14 , wherein the risk management machine learning model is a convolutional neural network model.
17 . The computer-implemented system according to claim 16 , wherein at least one of a height or a width of a convolutional kernel of a convolutional layer in the convolutional neural network model is equal to a quantity of the plurality of feature types.
18 . The computer-implemented system according to claim 13 , wherein the operations further comprise, after training the risk management machine learning model using the training samples:
obtaining service data generated during a specified time period that describes a transaction activity, and partitioning the specified time period into one or more sub time periods;
for each sub time period, determining respective features of the service data in the sub time period;
generating a plurality of feature sequences, comprising, for each feature type:
sorting the respective features of the service data belonging to the feature type in the one or more sub time periods based on a corresponding sorting rule; and
classifying whether the transaction activity described by the service data is legal by inputting the plurality of feature sequences into the risk management machine learning model to generate a classification output.