CN111105241A - Identification method for anti-fraud of credit card transaction - Google Patents

Identification method for anti-fraud of credit card transaction Download PDF

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CN111105241A
CN111105241A CN201911323155.8A CN201911323155A CN111105241A CN 111105241 A CN111105241 A CN 111105241A CN 201911323155 A CN201911323155 A CN 201911323155A CN 111105241 A CN111105241 A CN 111105241A
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formula
transaction
fraud
credit card
identification method
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CN111105241B (en
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董雪梅
崔奔雷
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Zhejiang Gongshang University
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    • 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
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
    • 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
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers

Abstract

The invention discloses an identification method applied to credit card transaction anti-fraud, which specifically comprises the following steps: 101) aggregating transaction characteristics according to the identity characteristics; 102) aggregating transaction time characteristics according to regions; 103) a prediction model processing step and 104) a prediction result step; the invention provides an anti-fraud identification method applied to credit card transactions, which is based on fusion of multiple gradient lifting tree models.

Description

Identification method for anti-fraud of credit card transaction
Technical Field
The present invention relates to the field of credit cards, and more particularly, to a method for identifying anti-fraud in credit card transactions.
Background
As the internet financial industry develops, the situation of performing financial service transactions through internet channels is becoming more and more popular. For both internet transaction parties, it is particularly important to be able to correctly evaluate transaction risks and prevent financial fraud and other situations from occurring in a wind control work.
For credit investigation examination and anti-fraud tests of internet financial users, various credit investigation and evaluation materials of the users need to be examined and examined, so that transaction risks are evaluated, and benefits of financial platforms are guaranteed. At present, corresponding risk examination work also needs manual access to different degrees, so that the efficiency and stability of business development are limited.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an anti-fraud identification method applied to credit card transaction based on the fusion of a plurality of gradient lifting tree models.
In order to solve the technical problems, the technical scheme of the invention is as follows:
an identification method applied to credit card transaction anti-fraud specifically comprises the following steps:
101) aggregating transaction characteristics according to the identity characteristics;
102) aggregating transaction time characteristics according to regions;
103) establishing three models based on XGboost, Catboost and LightGBM to predict credit card transactions to obtain a probability judgment value of fraud;
the XGboost model has the following specific processing formula:
Figure BDA0002327704870000011
in the formula
Figure BDA0002327704870000021
Is the term for the residual error,
Figure BDA0002327704870000022
is a regular term, wherein gamma is the number of decision trees, T is the number of leaf nodes,
Figure BDA0002327704870000023
lambda is a constant for the weight value of each leaf node;
will be shown in formula (1)
Figure BDA0002327704870000024
Instead, it is changed into
Figure BDA0002327704870000025
As a loss function in the formula
Figure BDA0002327704870000026
Instead, it is changed into
Figure BDA0002327704870000027
As a regular term in the formula, the formula after conversion is as follows:
Figure BDA0002327704870000028
in the formula
Figure BDA0002327704870000029
Is a newly added t-th tree, and the changed value is recorded as ft(xi) (ii) a Wherein the t-1 tree is fit to
Figure BDA00023277048700000210
Further decomposing the residual sum of squares of the previous t-1 trees, and the newly fitted t-th tree, convert equation (2) to the following equation:
Figure BDA00023277048700000211
so that each time a decision tree is found, f is madet(xi) The maximum residual value is reduced;
will be that in formula (3)
Figure BDA00023277048700000212
As x, then ft(xi) Δ x, obj (t) ═ F (x + Δ x), taylor expansion is performed, and this is described
Figure BDA00023277048700000213
To pair
Figure BDA00023277048700000214
Is noted as the first derivative ofiThe second derivative is denoted as hiIgnoring the constant component C, the following equation is obtained:
Figure BDA00023277048700000215
wherein f ist(xi) As a function of the leaf node weights based on the t-th tree, equation (4) is transformed as follows:
Figure BDA00023277048700000216
wherein
Figure BDA00023277048700000217
The samples are divided into leaf nodes, and the sequential traversal of the samples 1 to n is changed into the traversal from the sample on the leaf node 1 to the sample on the leaf node n, so that the following formula is obtained:
Figure BDA0002327704870000031
note the book
Figure BDA0002327704870000032
Is GiMemory for recording
Figure BDA0002327704870000033
Is HiIs converted into wjThe multivariate extreme value formula of (1):
Figure BDA0002327704870000034
the new objective function obtained by substituting equation (6) is:
Figure BDA0002327704870000035
according to the division of the leaf nodes, the divided part is divided into an L part and an R part, and the classified income formula is as follows:
Figure BDA0002327704870000036
obtaining a maximum fraud probability judgment value Gain of the XGboost;
104) and performing average weighted fusion on the results with low correlation according to the output results of the three models established in the step 103) by using a Pearson correlation coefficient matrix to obtain a final prediction result.
Further, the identification of the unique identity is determined based on the identification of the explicit and/or implicit identity characteristics of the client, and the transaction characteristics under the unique identity include the average amount of the transaction, the frequency of the transaction and the type of the used equipment.
Furthermore, the time characteristic is based on the time characteristic of the region, the time of the highest transaction frequency band is counted according to the region classification, and the difference value between each transaction time and the local high-frequency transaction time is calculated to serve as the important characteristic for judging the abnormal transaction.
Further, the Catboost model randomly orders the training set, and for the p-th sample, the statistical value of the previous p-1 sample values is used for replacing the p-th sample, and the specific formula is as follows:
Figure BDA0002327704870000037
p and a are hyper-parameters so as to reduce noise obtained in a low-frequency category, and the robustness and generalization capability of the model are improved in a sequencing promotion mode.
Further, the specific steps of step 104) are as follows:
401) acquiring Pearson correlation coefficients predicted by the three model outputs;
402) taking out the prediction result of which the Pearson correlation coefficient is lower than 0.99 in the step 401), wherein the prediction precision is close and excellent;
403) and fusing the prediction results of the three models by the same weight to output a final result as a final prediction result.
Compared with the prior art, the invention has the advantages that:
the invention has complementation among the characteristic types, and the real property of the software can be better found by fusing the characteristics of different abstraction layers. Furthermore, since the assumptions of learning algorithms are different, there is no learning algorithm that is optimal for various types of problems. It is not an easy task to select a suitable classification algorithm for different features. Different classification algorithms have induction bias, various learning algorithms can exert respective advantages by being fused, and the defects are overcome, so that the accuracy of the classification algorithms is improved, the false alarm rate is reduced, and the generalization performance of the classification algorithms is improved.
Detailed Description
The following specific embodiments are given to further illustrate the present invention.
An identification method applied to credit card transaction anti-fraud specifically comprises the following steps:
101) aggregating transaction characteristics according to the identity characteristics; the identification of the unique identity is identified based on the explicit and/or implicit identity characteristics of the client, and the transaction characteristics under the statistic unique identity comprise the average amount of the transaction, the frequency of the transaction and the type of the used equipment.
102) Aggregating transaction time characteristics according to regions; the time characteristic is based on the time characteristic of the region, the time of the highest transaction frequency band is counted according to the region classification, and the difference value between each transaction time and the local high-frequency transaction time is calculated to serve as the important characteristic for judging the abnormal transaction.
103) Establishing three models based on XGboost, Catboost and LightGBM to predict credit card transactions to obtain a probability judgment value of fraud;
the XGboost-based model has the following specific processing formula:
Figure BDA0002327704870000051
in the formula
Figure BDA0002327704870000052
Is the term for the residual error,
Figure BDA0002327704870000053
is a regular term, wherein gamma is the number of decision trees, T is the number of leaf nodes,
Figure BDA0002327704870000054
lambda is a constant for the weight value of each leaf node;
converting the formula (1) into the following formula (2), concretely
Figure BDA0002327704870000055
Is rewritten as
Figure BDA0002327704870000056
As a loss function, will
Figure BDA0002327704870000057
As a regularization term, rewrite to
Figure BDA0002327704870000058
Figure BDA0002327704870000059
In the formula
Figure BDA00023277048700000510
Is a newly added t-th tree, and the changed value is recorded as ft(xi) (ii) a Wherein the t-1 tree is fit to
Figure BDA00023277048700000511
Further decomposing the residual sum of squares of the previous t-1 trees, and the newly fitted t-th tree, convert equation (2) to the following equation:
Figure BDA00023277048700000512
so that each time a decision tree is found, f is madet(xi) The maximum residual value is reduced;
will be that in formula (3)
Figure BDA00023277048700000513
As x, then ft(xi) Δ x, obj (t) ═ F (x + Δ x), taylor expansion is performed, and this is described
Figure BDA00023277048700000514
To pair
Figure BDA00023277048700000515
Is noted as the first derivative ofiThe second derivative is denoted as hiIgnoring the constant component C, the following equation is obtained:
Figure BDA00023277048700000516
wherein f ist(xi) As a function of the leaf node weights based on the t-th tree, ft(xi) Is determined by the weight wqAnd (4) converting and expressing the formula (4) as the following formula:
Figure BDA00023277048700000517
wherein
Figure BDA0002327704870000061
The samples are divided into leaf nodes, and the sequential traversal of the samples 1 to n is changed into the traversal from the sample on the leaf node 1 to the sample on the leaf node n, so that the following formula is obtained:
Figure BDA0002327704870000062
note the book
Figure BDA0002327704870000063
Is GiMemory for recording
Figure BDA0002327704870000064
Is HiIs converted into wjThe multivariate extreme value formula of (1):
Figure BDA0002327704870000065
the new objective function obtained by substituting equation (6) is:
Figure BDA0002327704870000066
according to the division of the leaf nodes, dividing the divided part into an L part and an R part, and expressing the classified benefits as follows:
Figure BDA0002327704870000067
and traversing all possible conditions for each division, so that leaf nodes of each layer of each newly-built tree have the optimal weight coefficient, and the maximum fraud probability judgment value Gain based on the XGboost is obtained.
Randomly ordering the training set based on a Catboost model, and replacing the p sample with the statistical value of the previous p-1 sample values for the p sample, wherein the specific formula is as follows:
Figure BDA0002327704870000068
p and a are hyper-parameters and are used for reducing noise obtained in a low-frequency category, and the robustness and the generalization capability of the model are improved in a sequencing and promoting mode. Because the Catboost has great advantages in processing the classified data, the general processing of the classified data can be performed by adopting a coding (such as one-hot coding) mode and the like, but the scheme adopts a more effective strategy on a Catboost model, randomly orders a training set, improves the problem of prediction offset in the GBDT, and replaces a gradient calculation method (calculating gradient by using the same data set every time) in the GBDT by an ordered boosting mode, thereby achieving the effect of reducing gradient estimation deviation and improving the robustness and generalization capability of the model.
LightGBM directly adopts an improved algorithm of GBDT algorithm proposed by Microsoft in 2015, and has the main innovation point that the method reduces the sample size, reduces the calculation overhead and ensures the considerable accuracy rate by adopting a Gradient-based One-Side Sampling (GOSS) technology and an independent Feature merging (EFB) technology.
104) Obtaining a Pearson correlation coefficient matrix according to the output results of the three models established in the step 103), and performing average weighted fusion on the results with low correlation to obtain a final prediction result. The specific process is as follows:
401) acquiring Pearson correlation coefficients predicted by the three model outputs;
402) and (4) taking out the prediction result of which the Pearson correlation coefficient is lower than 0.99 in the step 401), and the prediction accuracy is close and better. Such as: in the scheme, based on the probability judgment value of the fraud of the XGboost model, the obtained Pearson correlation coefficient performance reaches 0.95, based on the probability judgment values of the fraud of the LightGBM and the Catboost model, the obtained Pearson correlation coefficient performance reaches 0.945, the probability judgment values of the fraud of the three models are close to each other, but the correlation coefficient ratio of the output result is lower, and then fusion is needed.
403) Fusing the probability judgment values of the fraud of the three models in the step 402) by the same weight to output a final result as a final prediction result. Such as: the probability judgment values of the output fraud of the three models are y1, y2 and y3 respectively. The final output is 1/3 by y1+1/3 by y2+1/3 by y 3.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the spirit of the present invention, and these modifications and decorations should also be regarded as being within the scope of the present invention.

Claims (5)

1. An identification method applied to credit card transaction anti-fraud is characterized by comprising the following steps:
101) aggregating transaction characteristics according to the identity characteristics;
102) aggregating transaction time characteristics according to regions;
103) establishing three models based on XGboost, Catboost and LightGBM to predict credit card transactions to obtain a probability judgment value of fraud;
the XGboost model has the following specific processing formula:
Figure FDA0002327704860000011
in the formulaIs the term for the residual error,
Figure FDA0002327704860000013
is a regular term, wherein gamma is the number of decision trees, T is the number of leaf nodes,
Figure FDA0002327704860000014
lambda is a constant for the weight value of each leaf node;
will be shown in formula (1)
Figure FDA0002327704860000015
Instead, it is changed into
Figure FDA0002327704860000016
As a loss function in the formula
Figure FDA0002327704860000017
Instead, it is changed into
Figure FDA0002327704860000018
As a regular term in the formula, the formula after conversion is as follows:
Figure FDA0002327704860000019
in the formula
Figure FDA00023277048600000110
Is a newly added t-th tree, and the changed value is recorded as ft(xi) (ii) a Wherein the t-1 tree is fit to
Figure FDA00023277048600000111
Further decomposing the residual sum of squares of the previous t-1 trees, and the newly fitted t-th tree, convert equation (2) to the following equation:
Figure FDA00023277048600000112
so that each time a decision tree is found, f is madet(xi) The maximum residual value is reduced;
will be that in formula (3)
Figure FDA00023277048600000113
As x, then ft(xi) Δ x, obj (t) ═ F (x + Δ x), taylor expansion is performed, and this is described
Figure FDA00023277048600000114
To pair
Figure FDA00023277048600000115
Is noted as the first derivative ofiThe second derivative is denoted as hiIgnoring the constant component C, the following equation is obtained:
Figure FDA0002327704860000021
wherein f ist(xi) As a function of the leaf node weights based on the t-th tree, equation (4) is transformed as follows:
Figure FDA0002327704860000022
wherein
Figure FDA0002327704860000023
The samples are divided into leaf nodes, and the sequential traversal of the samples 1 to n is changed into the traversal from the sample on the leaf node 1 to the sample on the leaf node n, so as to obtain the result ofThe following formula:
Figure FDA0002327704860000024
note the book
Figure FDA0002327704860000025
Is GiMemory for recording
Figure FDA0002327704860000026
Is HiIs converted into wjThe multivariate extreme value formula of (1):
Figure FDA0002327704860000027
the new objective function obtained by substituting equation (6) is:
Figure FDA0002327704860000028
according to the division of the leaf nodes, the divided part is divided into an L part and an R part, and the classified income formula is as follows:
Figure FDA0002327704860000029
obtaining a maximum fraud probability judgment value Gain of the XGboost;
104) and performing average weighted fusion on the results with low correlation according to the output results of the three models established in the step 103) by using a Pearson correlation coefficient matrix to obtain a final prediction result.
2. An identification method applied to credit card transaction anti-fraud according to claim 1, characterized in that: the identification of the unique identity is determined based on the identification of the explicit and/or implicit identity characteristics of the client, and the transaction characteristics under the statistical unique identity comprise the average amount of the transaction, the frequency of the transaction and the type of the used equipment.
3. An identification method applied to credit card transaction anti-fraud according to claim 1, characterized in that: the time characteristic is based on the time characteristic of the region, the time of the highest transaction frequency band is counted according to the region classification, and the difference value between each transaction time and the local high-frequency transaction time is calculated to serve as the important characteristic for judging the abnormal transaction.
4. An identification method applied to credit card transaction anti-fraud according to claim 1, characterized in that: the Catboost model randomly orders the training set, and for the p-th sample, the statistical value of the previous p-1 sample values is used for replacing the p-th sample, and the specific formula is as follows:
Figure FDA0002327704860000031
p and a are hyper-parameters so as to reduce noise obtained in a low-frequency category, and the robustness and generalization capability of the model are improved in a sequencing promotion mode.
5. An identification method applied to credit card transaction anti-fraud according to claim 1, characterized in that: step 104) comprises the following specific steps:
401) acquiring Pearson correlation coefficients predicted by the three model outputs;
402) taking out the prediction result of which the Pearson correlation coefficient is lower than 0.99 in the step 401), and the prediction precision is close and better;
403) and fusing the prediction results of the three models by the same weight to output a final result as a final prediction result.
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