SYSTEM AND METHOD FOR GENERATING RISK-CONTROL RULES

1 . A computer-implemented method, comprising:

obtaining a first data set and a second data set, wherein the first data set is associated with a first set of events in a first domain, and wherein the second data set is associated with a second set of events in a second domain;

combining the first data set and the second data set to generate a sample data set;

training a statistical model by applying the sample data set to determine a set of weights;

determining a set of characteristic parameter values and a set of conditions based on the set of weights;

generating a set of risk-control rules based on the set of conditions and the set of characteristic parameter values; and

applying the set of risk-control rules to a current event in the second domain to determine a credibility of the current event.

2 . The method of claim 1 , wherein combining the first data set and the second data set to generate the sample data set comprises:

identifying data with one or more of:

identical dimensions; and

identical service logic definition in the first domain and the second domain.

3 . The method of claim 1 , wherein training the statistical model by applying the sample data set to determine the set of weights comprises:

initializing a classification model with an initial set of weights based on the sample data set; and

adjusting the initial set of weights until a classification correction rate associated with the classification model satisfies a pre-defined convergence threshold value to obtain the set of weights.

4 . The method of claim 3 , wherein adjusting the initial set of weights further comprises:

decreasing a first subset of weights corresponding to a first portion of the sample data set that is misclassified, wherein the first portion of the sample data set is associated with a first domain; and

increasing a second subset of weights corresponding to a second portion of the sample data set that is misclassified, wherein the second portion of the sample data set is associated with a second domain.

5 . The method of claim 1 , wherein training the statistical model by applying the sample data set to determine the set of weights is based on a Transfer Adaptive Boosting (TrAdaBoost) technique; and

wherein the set of conditions is determined by applying a weighted decision tree algorithm.

6 . The method of claim 1 , wherein the first data set and the second data set include customer relationship management Recency Frequency Monetary (RFM) data used for indicating risk similarity in transaction events.

7 . The method of claim 6 , wherein the customer relationship management RFM data includes one or more of:

transaction related parameters;

internet risk related parameters; and

historical behavior related parameters.

8 . The method of claim 1 , wherein the first domain represents a well-established financial service with large amount of historical transaction data; and

wherein the second domain represents a new financial service with significantly less transaction data compared to that in the first domain.

9 . A computer system, comprising:

a processor; and

a storage device coupled to the processor and storing instructions which when executed by the processor cause the processor to perform a method, the method comprising

obtaining a first data set and a second data set, wherein the first data set is associated with a first set of events in a first domain, and wherein the second data set is associated with a second set of events in a second domain;

combining the first data set and the second data set to generate a sample data set;

training a statistical model by applying the sample data set to determine a set of weights;

determining a set of characteristic parameter values and a set of conditions based on the set of weights;

generating a set of risk-control rules based on the set of conditions and the set of characteristic parameter values; and

applying the set of risk-control rules to a current event in the second domain to determine a credibility of the current event.

10 . The computer system of claim 9 , wherein combining the first data set and the second data set to generate the sample data set comprises:

identifying data with one or more of:

identical dimensions; and

identical service logic definition in the first domain and the second domain.

11 . The computer system of claim 9 , wherein training the statistical model by applying the sample data set to determine the set of weights comprises:

initializing a classification model with an initial set of weights based on the sample data set; and

adjusting the initial set of weights until a classification correction rate associated with the classification model satisfies a pre-defined convergence threshold value to obtain the set of weights.

12 . The computer system of claim 11 , wherein adjusting the initial set of weights further comprises:

decreasing a first subset of weights corresponding to a first portion of the sample data set that is misclassified, wherein the first portion of the sample data set is associated with a first domain; and

increasing a second subset of weights corresponding to a second portion of the sample data set that is misclassified, wherein the second portion of the sample data set is associated with a second domain.

13 . The computer system of claim 9 , wherein training the statistical model by applying the sample data set to determine the set of weights is based on a Transfer Adaptive Boosting (TrAdaBoost) technique; and

wherein the set of conditions is determined by applying a weighted decision tree algorithm.

14 . The computer system of claim 9 , wherein the first data set and the second data set include customer relationship management Recency Frequency Monetary (RFM) data used for indicating risk similarity in transaction events.

15 . The computer system of claim 14 , wherein the customer relationship management RFM data includes one or more of:

transaction related parameters;

internet risk related parameters; and

historical behavior related parameters.

16 . The computer system of claim 9 , wherein the first domain represents a well-established financial service with large amount of historical transaction data; and

wherein the second domain represents a new financial service with significantly less transaction data compared to that in the first domain.

17 . A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:

obtaining a first data set and a second data set, wherein the first data set is associated with a first set of events in a first domain, and wherein the second data set is associated with a second set of events in a second domain;

combining the first data set and the second data set to generate a sample data set;

training a statistical model by applying the sample data set to determine a set of weights;

determining a set of characteristic parameter values and a set of conditions based on the set of weights;

generating a set of risk-control rules based on the set of conditions and the set of characteristic parameter values; and

applying the set of risk-control rules to a current event in the second domain to determine a credibility of the current event.

18 . The non-transitory computer-readable storage medium of claim 17 , wherein combining the first data set and the second data set to generate the sample data set comprises:

identifying data with one or more of:

identical dimensions; and

identical service logic definition in the first domain and the second domain.

19 . The non-transitory computer-readable storage medium of claim 17 , wherein training the statistical model by applying the sample data set to determine the set of weights comprises:

initializing a classification model with an initial set of weights based on the sample data set; and

adjusting the initial set of weights until a classification correction rate associated with the classification model satisfies a pre-defined convergence threshold value to obtain the set of weights.

20 . The non-transitory computer-readable storage medium of claim 19 , wherein adjusting the initial set of weights further comprises:

decreasing a first subset of weights corresponding to a first portion of the sample data set that is misclassified, wherein the first portion of the sample data set is associated with a first domain; and

increasing a second subset of weights corresponding to a second portion of the sample data set that is misclassified, wherein the second portion of the sample data set is associated with a second domain.