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Credit fraud detection method and system

A detection method and detection system technology, applied in the credit field, can solve the problem of incompatibility between accuracy and training speed, and achieve the effects of simple structure, saving computing resources, and improving training speed and classification speed.

Pending Publication Date: 2020-06-16
BEIJING INFORMATION SCI & TECH UNIV
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AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a credit fraud detection method and system, which solves the problem that the accuracy rate of the existing credit fraud detection method is not compatible with the training speed

Method used

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  • Credit fraud detection method and system
  • Credit fraud detection method and system

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Experimental program
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Effect test

specific Embodiment approach

[0108] Obtain credit sample data. The credit sample data is the transaction data of European credit card holders in September 2013. The credit sample data comes from the research group (Worldline and the Machine Learning) at Université Libre de Bruxelles (ULB), Belgium. Group).

[0109] PCA transformation is used to reduce the dimensionality of the credit sample data, and the dimensionality-reduced credit sample data is obtained. The dimensionality-reduced credit sample data contains 284,807 transaction records, and each transaction record contains 31 fields. For specific information, see Table 1, and each row of record fields A feature of:

[0110] Table 1 Specific information of a transaction record

[0111]

[0112]

[0113] In Table 1, No. represents the number, Time represents the time, Float represents the data type is a floating-point type, V1-V28 represents the attribute name, Amount represents the amount, and Class represents the category.

[0114] According t...

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Abstract

The invention discloses a credit fraud detection method and system, and relates to the field of credit. The method comprises the steps of obtaining credit sample data, dividing the credit sample datainto a training set and a test set, training a feature data window and a logistic regression model by using the training set to obtain a credit fraud detection initial model, debugging the credit fraud detection initial model by using the test set to obtain a credit fraud detection model, obtaining credit data to be tested, inputting the credit data to be detected into the credit fraud detection model to obtain a logistic regression classification result, and performing decision fusion on the logistic regression classification result to obtain a credit fraud detection result. According to thecredit fraud detection method and system, feature scanning and logistic regression are combined, feature scanning and logistic regression are performed on the to-be-detected credit data, and finally decision fusion is performed, so that the accuracy of credit fraud detection is improved, the credit fraud detection model is simple in structure, and the training speed and the classification speed are improved.

Description

technical field [0001] The invention relates to the field of credit, in particular to a credit fraud detection method and system. Background technique [0002] At present, there are two main methods for credit fraud detection: anti-fraud methods based on rule engines and anti-fraud methods based on machine learning. However, the rule engine relies heavily on the experience and lessons of experts, and the effect of the rule system formulated by different decision makers is often quite different, so the rule engine has great limitations. Machine learning has a wide range of applications in the field of anti-fraud, and classification algorithms such as logistic regression, support vector machines, and random forests are the most common; in addition, algorithms such as frequent itemset mining and neural networks also have good results in the field of anti-fraud. However, for models with simple structures such as linear regression and logistic regression, the accuracy of the mod...

Claims

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Application Information

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IPC IPC(8): G06Q40/02G06K9/62
CPCG06Q40/03G06F18/241G06F18/214
Inventor 康海燕张浩
Owner BEIJING INFORMATION SCI & TECH UNIV
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