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US 10,592,783 B2Granted
Risky transaction identification method and apparatus
AssigneeAlibaba Group Holding Limited
InventorsWenhao Zheng, Yalin Zhang, Longfei Li
Life of this patent
Estimating the term…
Abstract
A feature extraction is performed on transaction data to obtain a user classification feature and a transaction classification feature. A first dimension feature is constructed based on the user classification feature and the transaction classification feature. A dimension reduction processing is performed on the first dimension feature to obtain a second dimension feature. A probability that the transaction data relates to a risky transaction is determined based on a decision classification of the second dimension feature, where the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.
Claims17
A computer-implemented method of identifying a risky transaction, comprising:
- obtaining transaction data describing a transaction initiated by a user of a transaction service;
- extracting, from the obtained transaction data, feature data having a first dimension, the feature data comprising a plurality of user classification features and a plurality of transaction classification features;
- performing a dimension reduction process on the feature data having the first dimension to obtain sampled feature data having a second dimension, comprising:
- for each of the plurality of user classification features:
- determining a respective first feature sampling frequency;
- and
- sampling, from the plurality of user classification features and in accordance with the respective first feature sampling frequencies, a plurality of sampled user classification features;
- for each of the plurality of transaction classification features:
- determining a respective second feature sampling frequency;
- and
- sampling, from the plurality of transaction classification features and in accordance with the respective second feature sampling frequencies, a plurality of sampled transaction classification features;
- and
- determining a probability that the transaction data relates to a risky transaction based on a decision classification of the sampled feature data, wherein the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.
10 more elements
A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations to identify a risky transaction comprising:
- obtaining transaction data describing a transaction initiated by a user of a transaction service;
- extracting, from the obtained transaction data, feature data having a first dimension, the feature data comprising a plurality of user classification features and a plurality of transaction classification features;
- performing a dimension reduction process on the feature data having the first dimension to obtain a sampled feature data having a second dimension, comprising:
- for each of the plurality of user classification features:
- determining a respective first feature sampling frequency;
- and
- sampling, from the plurality of user classification features and in accordance with the respective first feature sampling frequencies, a plurality of sampled user classification features;
- for each of the plurality of transaction classification features:
- determining a respective second feature sampling frequency;
- and
- sampling, from the plurality of transaction classification features and in accordance with the respective second feature sampling frequencies, a plurality of sampled transaction classification features;
- and
- determining a probability that the transaction data relates to a risky transaction based on a decision classification of the sampled feature data, wherein the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.
10 more elements
A computer-implemented system of identifying a risky transaction, 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 transaction data describing a transaction initiated by a user of a transaction service;
- extracting, from the obtained transaction data, feature data having a first dimension, the feature data comprising a plurality of user classification features and a plurality of transaction classification features;
- performing a dimension reduction process on the feature data having the first dimension to obtain sampled feature data having a second dimension, comprising:
- for each of the plurality of user classification features:
- determining a respective first feature sampling frequency;
- and
- sampling, from the plurality of user classification features and in accordance with the respective first feature sampling frequencies, a plurality of sampled user classification features;
- for each of the plurality of transaction classification features:
- determining a respective second feature sampling frequency;
- and
- sampling, from the plurality of transaction classification features and in accordance with the respective second feature sampling frequencies, a plurality of sampled transaction classification features;
- and
- determining a probability that the transaction data relates to a risky transaction based on a decision classification of the sampled feature data, wherein the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.
13 more elements
Dependencies are inferred from the claim text.