CN110321950A - A kind of credit card fraud recognition methods - Google Patents

A kind of credit card fraud recognition methods Download PDF

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Publication number
CN110321950A
CN110321950A CN201910581816.0A CN201910581816A CN110321950A CN 110321950 A CN110321950 A CN 110321950A CN 201910581816 A CN201910581816 A CN 201910581816A CN 110321950 A CN110321950 A CN 110321950A
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Prior art keywords
training set
data
fraud
recognition methods
credit card
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CN201910581816.0A
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Chinese (zh)
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高中文
王天健
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Harbin University of Science and Technology
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Harbin University of Science and Technology
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Priority to CN201910581816.0A priority Critical patent/CN110321950A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/018—Certifying business or products
    • G06Q30/0185—Product, service or business identity fraud
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
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  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
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  • Development Economics (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Technology Law (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

The invention discloses a kind of credit card fraud recognition methods, are related to credit card techniques field；Its recognition methods are as follows: after carrying out desensitization process to the data of acquisition, be divided into training set and test set；Partial noise is added to the fraud data in training set, new fraud data is generated and is added in training set, solve training set imbalance problem；Feature extraction is carried out to training set and test set respectively using PCA method later, after obtaining feature vector, classifier is constructed using RF algorithm according to training set characteristic；Test set characteristic is imported into RF classifier, fraudulent trading is identified and is classified；Last utilization cost appraisal procedure assesses classification results, obtains fraud recognition methods performance；The present invention can be realized Quick Acquisition and pretreatment information, while generating new fraud data and training set is added；It is easy to implement modeling and classification, while can be realized the assessment of data, improves efficiency.

Description

A kind of credit card fraud recognition methods
Technical field
The invention belongs to credit card techniques fields, and in particular to a kind of credit card fraud recognition methods.
Background technique
Credit card is called credit card.It is a kind of mode of non-cash transaction payment, is simple credit service.Credit card by Bank or credit card company issue holder according to the credit rating and financial resources of user, and holder need not payment when holding credit card purchase Cash is refunded again when bill day.
There is the behavior of fraud in existing credit card, and often start a leak in identification, lead to not detect, together When low efficiency.
Summary of the invention
It to solve the behavior that existing credit card has fraud, and often starts a leak in identification, leads to not examine It measures, while the problem of low efficiency；The purpose of the present invention is to provide a kind of credit card fraud recognition methods.
A kind of credit card fraud recognition methods of the invention, its recognition methods are as follows: desensitize to the data of acquisition After processing, it is divided into training set and test set；Partial noise is added to the fraud data in training set, generates new fraud data It is added in training set, solves training set imbalance problem；Feature is carried out to training set and test set respectively using PCA method later It extracts, after obtaining feature vector, classifier is constructed using RF algorithm according to training set characteristic；Test set characteristic is led Enter RF classifier, fraudulent trading is identified and classifies；Last utilization cost appraisal procedure assesses classification results, obtains Cheat recognition methods performance.
A kind of credit card fraud recognition methods, its specific identification process are as follows:
Step 1: data prediction:
(1.1), data acquire: the data of acquisition are stored in database；
(1.2), sample data: first carrying out over-sampling and feature extraction, and then over-sampling is input to training set, feature extraction input To test set；
Step 2: algorithm design: including feature extraction, fraud identification, model evaluation；
(2.1), feature extraction: sampling PCA is electronic to carry out feature extraction to training set and test set respectively, obtains feature vector Afterwards, classifier is constructed using RF algorithm according to training set characteristic；
(2.2), fraud identification: RF model is established, while carrying out model measurement；
(2.3), model evaluation: utilization cost appraisal procedure assesses classification results, obtains fraud recognition methods performance.
Compared with prior art, the invention has the benefit that
One, Quick Acquisition and pretreatment information be can be realized, while generating new fraud data and training set is added；
Two, it is easy to implement modeling and classification, while can be realized the assessment of data, improves efficiency.
Detailed description of the invention
Detailed description will be given by the following detailed implementation and drawings by the present invention for ease of explanation,.
Fig. 1 is the structural diagram of the present invention.
In figure: 1-；2-；3-；4-；5-；6-；7-；8-；9-；10-；11-；12-；13-；14-；15-.
Specific embodiment
In order to make the objectives, technical solutions and advantages of the present invention clearer, below by shown in the accompanying drawings specific Embodiment describes the present invention.However, it should be understood that these descriptions are merely illustrative, and it is not intended to limit model of the invention It encloses.In addition, in the following description, descriptions of well-known structures and technologies are omitted, it is of the invention to avoid unnecessarily obscuring Concept.
Here, it should also be noted that, in order to avoid having obscured the present invention because of unnecessary details, in the accompanying drawings only Show with closely related structure and/or processing step according to the solution of the present invention, and be omitted little with relationship of the present invention Other details.
As shown in Figure 1, present embodiment uses following technical scheme: its recognition methods are as follows: in the number to acquisition After carrying out desensitization process, it is divided into training set and test set；Partial noise is added to the fraud data in training set, is generated new Fraud data be added training set in, solve training set imbalance problem；Later using PCA method respectively to training set and test Collection carries out feature extraction, after obtaining feature vector, constructs classifier using RF algorithm according to training set characteristic；By test set Characteristic imports RF classifier, identifies and classifies to fraudulent trading；Last utilization cost appraisal procedure to classification results into Row assessment obtains fraud recognition methods performance.
A kind of credit card fraud recognition methods, its specific identification process are as follows:
Step 1: data prediction:
(1.1), data acquire: the data of acquisition are stored in database；
(1.2), sample data: first carrying out over-sampling and feature extraction, and then over-sampling is input to training set, feature extraction input To test set；
Step 2: algorithm design: including feature extraction, fraud identification, model evaluation；
(2.1), feature extraction: sampling PCA is electronic to carry out feature extraction to training set and test set respectively, obtains feature vector Afterwards, classifier is constructed using RF algorithm according to training set characteristic；
(2.2), fraud identification: RF model is established, while carrying out model measurement；
(2.3), model evaluation: utilization cost appraisal procedure assesses classification results, obtains fraud recognition methods performance.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims Variation is included within the present invention.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art The other embodiments being understood that.

Claims (2)

1. a kind of credit card fraud recognition methods, it is characterised in that: its recognition methods are as follows: desensitize to the data of acquisition After processing, it is divided into training set and test set；Partial noise is added to the fraud data in training set, generates new fraud data It is added in training set, solves training set imbalance problem；Feature is carried out to training set and test set respectively using PCA method later It extracts, after obtaining feature vector, classifier is constructed using RF algorithm according to training set characteristic；Test set characteristic is led Enter RF classifier, fraudulent trading is identified and classifies；Last utilization cost appraisal procedure assesses classification results, obtains Cheat recognition methods performance.
2. a kind of credit card fraud recognition methods according to claim 1, it is characterised in that: its specific identification process It is as follows:
Step 1: data prediction:
(1.1), data acquire: the data of acquisition are stored in database；
(1.2), sample data: first carrying out over-sampling and feature extraction, and then over-sampling is input to training set, feature extraction input To test set；
Step 2: algorithm design: including feature extraction, fraud identification, model evaluation；
(2.1), feature extraction: sampling PCA is electronic to carry out feature extraction to training set and test set respectively, obtains feature vector Afterwards, classifier is constructed using RF algorithm according to training set characteristic；
(2.2), fraud identification: RF model is established, while carrying out model measurement；
(2.3), model evaluation: utilization cost appraisal procedure assesses classification results, obtains fraud recognition methods performance.
CN201910581816.0A 2019-06-30 2019-06-30 A kind of credit card fraud recognition methods Pending CN110321950A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110796349A (en) * 2019-10-16 2020-02-14 昆明理工大学 Credit card embezzlement event early warning model establishing and evaluating method
CN112270548A (en) * 2020-11-17 2021-01-26 中国人民解放军国防科技大学 Credit card fraud detection method based on deep learning

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103632168A (en) * 2013-12-09 2014-03-12 天津工业大学 Classifier integration method for machine learning
CN103678659A (en) * 2013-12-24 2014-03-26 焦点科技股份有限公司 E-commerce website cheat user identification method and system based on random forest algorithm
CN103902979A (en) * 2014-04-01 2014-07-02 浙江大学 Human face feature extraction and classification method
CN106547852A (en) * 2016-10-19 2017-03-29 腾讯科技（深圳）有限公司 Abnormal deviation data examination method and device, data preprocessing method and system
CN107395590A (en) * 2017-07-19 2017-11-24 福州大学 A kind of intrusion detection method classified based on PCA and random forest
US20180176243A1 (en) * 2016-12-16 2018-06-21 Patternex, Inc. Method and system for learning representations for log data in cybersecurity

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103632168A (en) * 2013-12-09 2014-03-12 天津工业大学 Classifier integration method for machine learning
CN103678659A (en) * 2013-12-24 2014-03-26 焦点科技股份有限公司 E-commerce website cheat user identification method and system based on random forest algorithm
CN103902979A (en) * 2014-04-01 2014-07-02 浙江大学 Human face feature extraction and classification method
CN106547852A (en) * 2016-10-19 2017-03-29 腾讯科技（深圳）有限公司 Abnormal deviation data examination method and device, data preprocessing method and system
US20180176243A1 (en) * 2016-12-16 2018-06-21 Patternex, Inc. Method and system for learning representations for log data in cybersecurity
CN107395590A (en) * 2017-07-19 2017-11-24 福州大学 A kind of intrusion detection method classified based on PCA and random forest

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
陈沁歆: "信用卡欺诈行为识别中的机器学习方法：比较研究", 《中国高新科技》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110796349A (en) * 2019-10-16 2020-02-14 昆明理工大学 Credit card embezzlement event early warning model establishing and evaluating method
CN112270548A (en) * 2020-11-17 2021-01-26 中国人民解放军国防科技大学 Credit card fraud detection method based on deep learning
CN112270548B (en) * 2020-11-17 2022-09-20 中国人民解放军国防科技大学 Credit card fraud detection method based on deep learning

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Inventor after: Gao Zhongwen

Inventor after: Wang Tianjian

Inventor before: Gao Zhongwen

Inventor before: Wang Tianjian

WD01 Invention patent application deemed withdrawn after publication
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Application publication date: 20191011