CN111798312A - Financial transaction system abnormity identification method based on isolated forest algorithm - Google Patents
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Abstract
A financial transaction system abnormity identification method based on an isolated forest algorithm relates to the technical field of financial wind control systems, and comprises the following steps: s1, carrying out consistency check on the original data, removing invalid data and repeated data, filling missing values, and converting the category variables into numerical type variables; s2, performing isolated forest modeling on the input data; s3, calculating the path length of the sample point in the isolated forest model and calculating the abnormal value score; and S4, setting a threshold value of the abnormal value score according to experience, judging the transaction behavior with the abnormal value score larger than the threshold value as the abnormal behavior, reporting the abnormal behavior to the verification module, and preventing the transaction risk by further security verification of the mobile phone verification code. The threshold requirement for inputting data is greatly reduced, more data can be input into the model, and more accurate results can be obtained. The limitation that the traditional supervised financial anomaly recognition model can only recognize historical existing fraudulent behavior can be reduced.
Description
Technical Field
The invention relates to the technical field of financial wind control systems, in particular to an improvement aspect of a data optimization processing method for abnormal identification of a financial transaction system in a financial wind control system.
Background
Wind control is one of the most important links in the financial field, and the level of financial wind control can be effectively improved by identifying the abnormity of financial transaction behaviors. Previous identification methods generally constructed supervised classification models for analysis, with risk being one class and no risk being the other class. This approach has two major drawbacks: first, such supervised models require tags that know in advance whether the user or transaction behavior is abnormal, such tagged data is not easily accessible in practice, and the amount that can be accessed is often not very large. Second, the data for such models comes from historical, existing data, which results in the models recognizing only existing fraud in the historical data, while new fraud is difficult to recognize because it is not in the historical data, which results in insufficient accuracy of the models.
Disclosure of Invention
The invention aims to overcome the defects of the existing abnormal recognition algorithm, and provides a financial transaction system abnormal recognition method based on an isolated forest algorithm, which is an unsupervised algorithm with high running efficiency. Under the condition that whether the financial transactions are abnormal or not is not given in advance, the probability of the financial transactions being abnormal is given by analyzing the rule of the financial transaction data, and the abnormal financial transactions are identified. And finally, reporting the abnormal transaction to a verification module for further security verification to achieve the purpose of better preventing the financial transaction risk.
In order to solve the technical problems provided by the invention, the technical scheme is as follows: a financial transaction system abnormity identification method based on an isolated forest algorithm is characterized in that: the method comprises the following steps:
s1, carrying out consistency check on the original data, removing invalid data and repeated data, filling missing values, and converting the category variables into numerical type variables;
s2, performing isolated forest modeling on the input data;
s3, calculating the path length of the sample point in the isolated forest model and calculating the abnormal value score;
and S4, setting a threshold value of the abnormal value score according to experience, judging the transaction behavior with the abnormal value score larger than the threshold value as the abnormal behavior, reporting the abnormal behavior to the verification module, and preventing the transaction risk by further security verification of the mobile phone verification code.
The technical scheme for further limiting the invention comprises the following steps:
the step S2 includes:
the isolated forest model is a tree type integrated model consisting of more than one isolated tree, and all nodes of each isolated tree have 2 child nodes or no child nodes;
given a set of n samples X ═ X1,x2,...,xnRecursion of a sample set X by randomly selecting a feature q of the data set and randomly selecting a split value p of the feature, thereby establishing an isolated tree;
the process of recursively building the orphan tree does not stop until one of three conditions is met: firstly, the depth of the isolated tree reaches a limited maximum value; secondly, only one sample is arranged in the node of the isolated tree after a certain recursion; after a certain recursion, the data contained in the nodes of the isolated tree have the same value;
the method comprises the steps of firstly sampling original data for t times, extracting a part of data each time to establish an isolated tree, and establishing t isolated trees by sampling for t times, wherein the t isolated trees form an isolated forest.
The step S3 includes:
the path length of a leaf node x in an isolated tree is defined as the number of edges that run through from the root node to the leaf node where x is located;
given n samples, an isolated tree is built for which the average path length c (n) of an isolated tree is defined as
H (i) ═ ln (i) + 0.5772156649; when the sample size is fixed to n, the average path lengths c (n) of different isolated trees are the same;
the outlier score s (x, n) for a sample point x is defined as
The isolated forest is composed of t isolated trees, for a certain sample x, the leaf node x of each isolated tree has a path length h (x), and E (h (x)) is the average value of the path lengths h (x) of the sample x in different isolated trees of the isolated forest; c (n) is the average path length of the isolated tree containing n samples;
and setting a threshold value of the abnormal value score, wherein the abnormal value score s (x, n) is 0< s (x, n) <1, and the larger the s (x, n) is, the more abnormal the points are, and judging the transaction behavior with the abnormal value score larger than the threshold value as abnormal transaction.
The invention has the beneficial effects that: the invention adopts an isolated forest algorithm, and is an efficient unsupervised abnormal value detection method. From the data acquirability perspective, the isolated forest model can be used for searching for abnormal financial transaction behaviors only by knowing the characteristics of the data samples and determining the class labels to which the sample points belong, the threshold requirement of the input data is greatly reduced, more data can be input into the model, and more accurate results can be obtained. From the recognizable content, when new abnormal behavior data is input into the isolated forest, the model is likely to recognize the new abnormal behavior data as an abnormal value (abnormal behavior), so that the isolated forest can reduce the limitation that the traditional supervised financial abnormal recognition model can only recognize the historical existing fraudulent behaviors. Therefore, the isolated forest is particularly suitable for transaction abnormity identification in the financial field, the isolated forest only has linear time complexity while ensuring high precision, and the calculated amount of the model is small.
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FIG. 1 is a flow chart of an isolated forest algorithm-based financial transaction system anomaly identification method of the present invention.
Detailed Description
In order that the invention may be more readily understood, reference will now be made in detail to specific embodiments thereof, and the accompanying drawings will be used to illustrate the invention:
referring to fig. 1, the invention relates to a financial transaction system abnormity identification method based on an isolated forest algorithm, which comprises the following steps:
and S1, performing consistency check on the original data, removing invalid data and repeated data, filling missing values according to actual conditions, and converting the category variables into numerical variables.
S2, performing isolated forest modeling on the input data;
the specific method for modeling the isolated forest comprises the following steps: the isolated forest model is a tree-type integrated model consisting of more than one isolated tree(ii) a For example, given a set of n samples X ═ { X1,x2,...,xnRecursion of a sample set X by randomly selecting a feature q of the data set and randomly selecting a split value p of the feature, thereby establishing an isolated tree; all nodes of each isolated tree have 2 child nodes or no child nodes;
the process of recursively building the orphan tree does not stop until one of three conditions is met: firstly, the depth of the isolated tree reaches a limited maximum value; secondly, only one sample is arranged in the node of the isolated tree after a certain recursion; after a certain recursion, the data contained in the nodes of the isolated tree have the same value;
sampling input original data samples for t times, extracting n data each time, and respectively establishing an isolated tree for the n data extracted each time, wherein the t isolated trees form an isolated forest.
S3, calculating the path length of the sample point in the isolated forest model and calculating the abnormal value score; points with higher scores are more likely to be anomalous transactions; a specific method, for example, defines the path length h (x) of the leaf node x in the isolated tree as the number of edges that run from the root node to the leaf node where the leaf node x is located;
s31, given n samples, establishing an isolated tree, defining the average path length c (n) of the isolated tree as
H (i) ═ ln (i) + 0.5772156649. When the sample size is fixed to n, c (n) of different isolated trees is the same;
s32, defining the abnormal value score S (x, n) of the sample point x as
The isolated forest is composed of a plurality of isolated trees, for a certain sample leaf node x, the leaf node x of each isolated tree has a path length h (x), and E (h (x)) is the average value of the path lengths h (x) of the leaf node x in different isolated trees of the isolated forest; c (n) is the average path length of the isolated tree containing n samples; the abnormal value score s (x, n) is 0<s(x,n)<The larger the 1, s (x, n), the more abnormalThe more likely it is an anomalous transaction.
S33, calculating the abnormal value score of each sample data according to the method of S32.
And S4, setting a threshold value of the abnormal value score according to experience, judging the transaction behavior with the abnormal value score larger than the threshold value as the abnormal behavior, reporting the abnormal behavior to the verification module, and preventing the transaction risk by further security verification of the mobile phone verification code.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. A financial transaction system abnormity identification method based on an isolated forest algorithm is characterized in that: the method comprises the following steps:
s1, carrying out consistency check on the original data, removing invalid data and repeated data, filling missing values, and converting the category variables into numerical type variables;
s2, performing isolated forest modeling on the input data;
s3, calculating the path length of the sample point in the isolated forest model and calculating the abnormal value score;
and S4, setting a threshold value of the abnormal value score according to experience, judging the transaction behavior with the abnormal value score larger than the threshold value as the abnormal behavior, reporting the abnormal behavior to the verification module, and preventing the transaction risk by further security verification of the mobile phone verification code.
2. The isolated forest algorithm-based financial transaction system anomaly identification method according to claim 1, wherein the step S2 comprises:
the isolated forest model is a tree type integrated model consisting of more than one isolated tree, and all nodes of each isolated tree have 2 child nodes or no child nodes;
given a set of n samples X ═ X1,x2,...,xnRecursion of a sample set X by randomly selecting a feature q of the data set and randomly selecting a split value p of the feature, thereby establishing an isolated tree;
the process of recursively building the orphan tree does not stop until one of three conditions is met: firstly, the depth of the isolated tree reaches a limited maximum value; secondly, only one sample is arranged in the node of the isolated tree after a certain recursion; after a certain recursion, the data contained in the nodes of the isolated tree have the same value;
the method comprises the steps of firstly sampling original data for t times, extracting a part of data each time to establish an isolated tree, and establishing t isolated trees by sampling for t times, wherein the t isolated trees form an isolated forest.
3. The isolated forest algorithm-based financial transaction system anomaly identification method according to claim 1, wherein the step S3 comprises:
the path length h (x) of a leaf node x in the isolated tree is defined as the number of edges that run through from the root node to the leaf node where x is located;
given n samples, an isolated tree is built for which the average path length c (n) of an isolated tree is defined as
H (i) ═ ln (i) + 0.5772156649; when the sample size is fixed to n, the average path lengths c (n) of different isolated trees are the same;
the outlier score s (x, n) for a sample point x is defined as
The isolated forest is composed of t isolated trees, for a certain sample x, the leaf node x of each isolated tree has a path length h (x), and E (h (x)) is the average value of the path lengths h (x) of the sample x in different isolated trees of the isolated forest; c (n) isAverage path length of an isolated tree containing n samples;
and setting a threshold value of the abnormal value score, wherein the abnormal value score s (x, n) is 0< s (x, n) <1, and the larger the s (x, n) is, the more abnormal the points are, and judging the transaction behavior with the abnormal value score larger than the threshold value as abnormal transaction.
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| CN114925196A (en) * | 2022-03-01 | 2022-08-19 | 健康云(上海)数字科技有限公司 | Diabetes blood test abnormal value auxiliary removing method under multilayer perception network |
| CN114925196B (en) * | 2022-03-01 | 2024-05-21 | 健康云(上海)数字科技有限公司 | Auxiliary eliminating method for abnormal blood test value of diabetes under multi-layer sensing network |
| CN117650971A (en) * | 2023-12-04 | 2024-03-05 | 武汉烽火技术服务有限公司 | Method and device for preventing equipment failure of communication system |
| CN117650971B (en) * | 2023-12-04 | 2024-06-14 | 武汉烽火技术服务有限公司 | Method and device for preventing equipment failure of communication system |
| CN117408734A (en) * | 2023-12-15 | 2024-01-16 | 广东云百科技有限公司 | Customer information intelligent management system based on Internet of things equipment |
| CN117408734B (en) * | 2023-12-15 | 2024-03-19 | 广东云百科技有限公司 | Customer information intelligent management system based on Internet of things equipment |
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