CN113610534B - Data processing method and device for anti-fraud - Google Patents
Data processing method and device for anti-fraud Download PDFInfo
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Abstract
The application discloses a data processing method and device for anti-fraud. The method comprises the following steps: analyzing transaction flow data provided by a user to obtain flow analysis data, calling transaction big data in an official institution of the user to be evaluated, analyzing the transaction big data to obtain transaction analysis data, preprocessing the transaction analysis data and the flow analysis data to obtain verification feature data, carrying out verification scoring processing on the verification feature data to obtain verification scoring data through a preset verification model, and matching corresponding business strategies according to the verification scoring data.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method and apparatus for anti-fraud data processing.
Background
With the improvement of the marketization level and the external opening degree of the financial industry, competition among financial institutions is increased, and financial services are closely related to lives of people. In many financial related services, transaction data is a significant information that plays a significant role in the assessment of consumer revenue, consumption and asset capabilities. If the transaction data is false, the corresponding risks such as deception credit and cash register are generated. How to effectively identify the true and false of the transaction data, avoiding the risk actions such as cheating credit, cash register and the like, and is very important in the financial service industry.
The current anti-fraud products are many, but transaction data of all industries are not communicated, the problem of data barriers is serious, and the identifiable risk types of the related anti-fraud products are single; and a lot of transaction data, especially data such as payroll income, mainly depend on manual discrimination, so that huge waste of human resources is caused.
In the prior art, the financial institution has the technical problem of low efficiency in identifying the fraud risk of the user.
Disclosure of Invention
The application mainly aims to provide a data processing method and device for anti-fraud, which solve the technical problem of low identification efficiency of user fraud risk due to data barriers in the prior art, so as to improve the identification efficiency of financial institutions on the user fraud risk.
In order to achieve the above object, the present application proposes a data processing method for anti-fraud.
According to a second aspect of the present application, a data processing apparatus for anti-fraud is presented.
According to a third aspect of the present application, a computer readable storage medium is presented.
According to a fourth aspect of the present application, an electronic device is presented.
In view of this, according to a first aspect of the present application, there is provided a data processing method for anti-fraud, comprising:
acquiring user transaction data, wherein the user transaction data is transaction stream data provided by a user to be evaluated;
carrying out stream analysis processing on the user transaction data to obtain stream analysis data;
based on a preset verification rule, carrying out verification processing on the stream analysis data to obtain authenticity scoring data;
and matching the business strategy corresponding to the authenticity grading data in a preset business strategy database.
Further, based on a preset verification rule, performing verification processing on the stream analysis data to obtain authenticity scoring data, including:
Acquiring transaction big data of the user to be evaluated;
screening the transaction big data based on a preset data screening rule to obtain transaction analysis data;
Preprocessing the stream analysis data and the transaction analysis data to obtain verification characteristic data;
and carrying out verification scoring processing on the verification characteristic data based on a preset verification model to obtain the authenticity scoring data.
Further, performing a stream analysis process on the user transaction data to obtain stream analysis data, including:
identifying the user transaction data to obtain process stream data;
based on a preset transaction extraction rule, extracting the process stream data to obtain process stream analysis data;
And carrying out standardized processing on the process flow analysis data to obtain the flow analysis data.
Further, acquiring transaction big data of the user to be evaluated, including:
Judging the user to be evaluated based on the three element rule of the bank card;
If the to-be-evaluated user meets the three-element rule of the bank card, calling income data, consumption data, operator data and travel class data of the to-be-evaluated user in an official institution;
and carrying out standardized processing on the income data, the consumption data, the operator data and the travel class data to obtain the transaction big data.
Further, preprocessing the stream analysis data and the transaction analysis data to obtain verification feature data, including:
based on the transaction analysis data, carrying out cross comparison on the stream analysis data to obtain comparison result data;
And if the comparison result data meets the preset condition, integrating the stream analysis data and the transaction analysis data to obtain the verification characteristic data.
Further, matching the business strategy corresponding to the authenticity scoring data in a preset business strategy database comprises the following steps:
If the authenticity grading data meets a preset passing rule, matching a paying-off strategy corresponding to the authenticity grading data;
If the authenticity grading data meets a preset reject rule, matching a reject strategy corresponding to the authenticity grading data;
And if the authenticity grading data meets a preset review rule, matching a review strategy corresponding to the authenticity grading data.
According to a second aspect of the present application, there is provided a data processing apparatus for anti-fraud, comprising:
The first data acquisition module is used for acquiring user transaction data, wherein the user transaction data is transaction stream data provided by a user to be evaluated;
the stream analysis module is used for carrying out stream analysis processing on the user transaction data to obtain stream analysis data;
the verification module is used for carrying out verification processing on the flow analysis data based on a preset verification rule to obtain authenticity scoring data;
and the result output module is used for matching the business strategy corresponding to the authenticity grading data in a preset business strategy database.
Further, the verification module includes:
the second data acquisition module is used for acquiring the transaction big data of the user to be evaluated;
The transaction analysis module is used for screening the transaction big data based on a preset data screening rule to obtain transaction analysis data;
The preprocessing module is used for preprocessing the stream analysis data and the transaction analysis data to obtain verification characteristic data;
and the verification scoring module is used for carrying out verification scoring processing on the verification characteristic data based on a preset verification model to obtain the authenticity scoring data.
According to a third aspect of the present application, a computer-readable storage medium storing computer instructions for causing a computer to execute the above-described data processing method for anti-fraud is provided.
According to a fourth aspect of the present application, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the data processing method for anti-fraud described above.
The technical scheme provided by the embodiment of the application can comprise the following beneficial effects:
According to the application, the transaction flow data of the user to be evaluated is called to carry out authenticity judgment, so that the technical problem that the efficiency of identifying the fraud risk of the user is low in the financial institution in the prior art is solved, and the technical effect of improving the efficiency of identifying the fraud risk of the user is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, are incorporated in and constitute a part of this specification. The drawings and their description are illustrative of the application and are not to be construed as unduly limiting the application. In the drawings:
FIG. 1 is a flow chart of a data processing method for anti-fraud according to the present application;
FIG. 2 is a flow chart of a data processing method for anti-fraud according to the present application;
FIG. 3 is a flow chart of a data processing method for anti-fraud according to the present application;
FIG. 4 is a schematic diagram of a data processing apparatus for anti-fraud according to the present application;
FIG. 5 is a schematic diagram of another data processing apparatus for anti-fraud according to the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the present application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate an azimuth or a positional relationship based on that shown in the drawings. These terms are only used to better describe the present application and its embodiments and are not intended to limit the scope of the indicated devices, elements or components to the particular orientations or to configure and operate in the particular orientations.
Also, some of the terms described above may be used to indicate other meanings in addition to orientation or positional relationships, for example, the term "upper" may also be used to indicate some sort of attachment or connection in some cases. The specific meaning of these terms in the present application will be understood by those of ordinary skill in the art according to the specific circumstances.
Furthermore, the terms "mounted," "configured," "provided," "connected," "coupled," and "sleeved" are to be construed broadly. For example, "connected" may be in a fixed connection, a removable connection, or a unitary construction; may be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements, or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
FIG. 1 is a schematic flow chart of a data processing method for anti-fraud according to the present application, as shown in FIG. 1, the method includes the following steps:
s101: acquiring user transaction data, wherein the user transaction data is transaction stream data provided by a user to be evaluated;
The user transaction data can be transaction flow data provided by a user, wherein the transaction flow data comprises paper flow receipts and electronic flow lists, and the specific forms can be formats of scanning pieces, pictures, excel, PDF and the like.
S102: carrying out stream analysis processing on the user transaction data to obtain stream analysis data;
FIG. 2 is a flow chart of a data processing method for anti-fraud according to the present application, as shown in FIG. 2, the method includes the following steps:
S201: identifying the user transaction data to obtain process stream data;
If the user transaction data is a paper running receipt uploaded in the forms of pictures, PDFs and the like, identifying the user transaction data by means of OCR text identification, framed form identification, frameless form identification, sliding template identification and the like;
if the user transaction data is an electronic flow list, the user transaction data is identified through Excel reading, data extraction and other modes.
Wherein the corresponding identification means may be matched based on the user transaction data form.
S202: based on a preset transaction extraction rule, extracting the process stream data to obtain process stream analysis data;
And extracting and analyzing the user transaction data to obtain user flow data with multiple dimensions such as account information, transaction amount, transaction type, opponent information, account balance, remark information and the like by combining the recognition results of the paper flow bill and the electronic flow bill.
S203: and carrying out standardized processing on the process flow analysis data to obtain the flow analysis data.
Because the user transaction data have different data formats, the process flow analysis book is standardized, for example, the process flow analysis data with uniform date format and uniform amount format is classified, and the process flow analysis data is standardized according to rules of transaction opponents, transaction time periods, balance types, amount statistics and the like, so as to obtain the flow analysis data.
S103: based on a preset verification rule, carrying out verification processing on the stream analysis data to obtain authenticity scoring data;
FIG. 3 is a flow chart of a data processing method for anti-fraud according to the present application, as shown in FIG. 3, the method includes the following steps:
s301: acquiring transaction big data of the user to be evaluated, wherein the transaction big data are transaction data of the user to be evaluated in an official institution;
Based on the three element rule of the bank card, judging the user data to be evaluated, and judging whether the name, the identity card number and the bank card number corresponding to the user to be evaluated are consistent;
If the to-be-evaluated user meets the three-element rule of the bank card, calling income data, consumption data, operator data and travel class data of the to-be-evaluated user in an official institution, wherein the official institution can be multi-official authority data such as China Union, three-network operator, social security, public accumulation, three-party transaction data, travel class data and the like; the revenue data dimension may be social security/public accumulation data, revenue assessment class data; the consumption data dimension can be Unionpay consumption data, payment treasures/WeChat consumption data; the operator data dimension may be three-network operator data; the travel class data dimension can be travel class consumption data of high-speed rail, airplane, travel and the like.
And analyzing transaction data corresponding to income data, consumption data, operator data and travel data dimensions of the user to be evaluated in the official institution through a computer vision and natural language processing (CV+NLP) algorithm, and carrying out standardized processing on the analyzed transaction big data according to a preset standardized rule to obtain the transaction big data.
S302: screening the transaction big data based on a preset data screening rule to obtain transaction analysis data;
The method comprises the steps of carrying out feature extraction, feature derivation and feature database construction on transaction big data based on a big data modeling feature engineering construction method, carrying out correlation test and multiple collinearity (VIF) test on features in a feature database based on an algorithm model such as a logistic regression model, carrying out feature screening according to preset screening conditions, carrying out classification integration on the transaction big data by combining basic dimensions such as time, space and transaction type, and carrying out formatting storage according to a certain data storage standard on the basis to obtain transaction analysis data.
S303: preprocessing the stream analysis data and the transaction analysis data to obtain verification characteristic data;
based on the transaction analysis data, carrying out cross comparison on the stream analysis data to obtain comparison result data;
According to a preset time period, transaction analysis data and stream analysis data in the preset time period are selected for cross comparison, and a comparison result is obtained, for example, the transaction analysis data and stream analysis data in the preset time period are selected, cross comparison is carried out according to priorities of transaction time, transaction amount interval, transaction type and the like, and a comparison result of whether the transaction date and day difference, the transaction amount difference, the transaction type and the like are consistent or not is obtained as a difference value of the stream analysis data and the transaction analysis data.
If the comparison result data meets the preset condition, integrating the flow analysis data and the transaction analysis data to obtain the verification feature data, and when the comparison result data is smaller than the preset difference condition, taking the flow analysis data and the transaction analysis data as basic dimensions, modeling through big data, combining the basic dimensions of time, space, transaction type and the like, classifying and integrating the flow analysis data and the transaction analysis data, and on the basis, deriving feature variables to perfect the whole data dimension to obtain the verification feature data.
If the comparison result data does not meet the preset condition, that is, the comparison result is larger than the preset difference condition, judging that the flow analysis data is false data, marking the flow analysis data, processing the transaction analysis data to obtain transaction characteristic data, recording the judgment result data, and deriving characteristic variables of the transaction analysis data to obtain the transaction characteristic data.
S304: and carrying out verification scoring processing on the verification characteristic data based on a preset verification model to obtain the authenticity scoring data.
Based on a preset verification model, the verification feature data is used as bottom data trained by an algorithm model, a corresponding algorithm model is established through algorithms such as logistic regression and xgboost, lightGBM, effective verification feature data are screened out, weight assignment is carried out on the effective verification feature data, different values correspond to different scores, finally, all the scores are summarized, verification scoring processing is carried out on the verification feature data, and authenticity scoring data are obtained, wherein the range of authenticity scoring is 0-100.
S104: and matching the business strategy corresponding to the authenticity grading data in a preset business strategy database.
If the authenticity grading data meets a preset passing rule, matching a paying-off strategy corresponding to the authenticity grading data;
If the authenticity grading data meets a preset reject rule, matching a reject strategy corresponding to the authenticity grading data;
And if the authenticity grading data meets a preset review rule, matching a review strategy corresponding to the authenticity grading data.
FIG. 4 is a schematic diagram of a data processing apparatus for anti-fraud according to the present application, as shown in FIG. 4, the apparatus includes:
a first data obtaining module 41, configured to obtain user transaction data, where the user transaction data is transaction stream data provided by a user to be evaluated;
The stream analysis module 42 is configured to perform stream analysis processing on the user transaction data to obtain stream analysis data;
the verification module 43 performs verification processing on the stream analysis data based on a preset verification rule to obtain authenticity scoring data;
And a result output module 44, configured to match the business strategy corresponding to the authenticity score data in a preset business strategy database.
FIG. 5 is a schematic diagram of another data processing apparatus for anti-fraud according to the present application, as shown in FIG. 5, the apparatus includes:
a second data obtaining module 51, configured to obtain big transaction data of the user to be evaluated, where the big transaction data is transaction data of the user to be evaluated in an official institution;
The transaction analysis module 52 performs screening processing on the transaction big data based on preset data screening rules to obtain transaction analysis data;
a preprocessing module 53, configured to preprocess the streaming analysis data and the transaction analysis data to obtain verification feature data;
The verification scoring module 54 performs verification scoring processing on the verification feature data based on a preset verification model, so as to obtain the authenticity scoring data.
The specific manner in which the operations of the units in the above embodiments are performed has been described in detail in the embodiments related to the method, and will not be described in detail here.
In summary, in the application, the transaction flow data provided by the user is analyzed to obtain flow analysis data, the transaction big data in the official institution of the user to be evaluated is called, the transaction big data is analyzed to obtain transaction analysis data, the transaction analysis data and the flow analysis data are preprocessed to obtain the verification feature data, the verification feature data is subjected to verification scoring processing through a preset verification model to obtain the verification scoring data, and the corresponding business strategy is matched according to the verification scoring data.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
It will be apparent to those skilled in the art that the elements or steps of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a memory device for execution by the computing devices, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (7)
1. A data processing method for anti-fraud, comprising:
acquiring user transaction data, wherein the user transaction data is transaction stream data provided by a user to be evaluated;
carrying out stream analysis processing on the user transaction data to obtain stream analysis data;
based on a preset verification rule, carrying out verification processing on the stream analysis data to obtain authenticity scoring data;
Matching the business strategy corresponding to the authenticity grading data in a preset business strategy database;
Based on a preset verification rule, carrying out verification processing on the stream analysis data to obtain authenticity scoring data, wherein the method comprises the following steps:
acquiring transaction big data of the user to be evaluated, wherein the transaction big data are transaction data of the user to be evaluated in an official institution;
screening the transaction big data based on a preset data screening rule to obtain transaction analysis data;
Preprocessing the stream analysis data and the transaction analysis data to obtain verification characteristic data;
based on a preset verification model, carrying out verification scoring processing on the verification feature data to obtain the authenticity scoring data;
preprocessing the stream analysis data and the transaction analysis data to obtain verification feature data, wherein the preprocessing comprises the following steps:
based on the transaction analysis data, carrying out cross comparison on the stream analysis data to obtain comparison result data;
according to a preset time period, selecting transaction analysis data in the preset time period to be compared with stream analysis data in a poor way, and obtaining a comparison result;
If the comparison result data meets the preset condition, integrating the flow analysis data and the transaction analysis data to obtain the verification feature data, and if the comparison result data is smaller than the preset difference condition, taking the flow analysis data and the transaction analysis data as basic dimensions, modeling through big data, combining the basic dimensions such as time, space and transaction type, classifying and integrating the flow analysis data and the transaction analysis data, and on the basis, deriving feature variables to perfect the whole data dimension to obtain the verification feature data;
if the comparison result data does not meet the preset condition, that is, the comparison result is larger than the preset difference condition, judging that the flow analysis data is false data, marking the flow analysis data, processing the transaction analysis data to obtain transaction characteristic data, recording the judgment result data, and deriving characteristic variables of the transaction analysis data to obtain the transaction characteristic data.
2. The data processing method according to claim 1, wherein performing a stream parsing process on the user transaction data to obtain stream parsed data, comprises:
identifying the user transaction data to obtain process stream data;
based on a preset transaction extraction rule, extracting the process stream data to obtain process stream analysis data;
And carrying out standardized processing on the process flow analysis data to obtain the flow analysis data.
3. The data processing method according to claim 1, wherein acquiring transaction big data of the user to be evaluated comprises:
Judging the user to be evaluated based on the three element rule of the bank card;
If the to-be-evaluated user meets the three-element rule of the bank card, calling income data, consumption data, operator data and travel class data of the to-be-evaluated user in an official institution;
and carrying out standardized processing on the income data, the consumption data, the operator data and the travel class data to obtain the transaction big data.
4. The data processing method according to claim 1, wherein matching the business policy corresponding to the authenticity score data in a preset business policy database includes:
If the authenticity grading data meets a preset passing rule, matching a paying-off strategy corresponding to the authenticity grading data;
If the authenticity grading data meets a preset reject rule, matching a reject strategy corresponding to the authenticity grading data;
And if the authenticity grading data meets a preset review rule, matching a review strategy corresponding to the authenticity grading data.
5. A data processing apparatus for anti-fraud, comprising:
The first data acquisition module is used for acquiring user transaction data, wherein the user transaction data is transaction stream data provided by a user to be evaluated;
the stream analysis module is used for carrying out stream analysis processing on the user transaction data to obtain stream analysis data;
the verification module is used for carrying out verification processing on the flow analysis data based on a preset verification rule to obtain authenticity scoring data;
The result output module is used for matching the business strategy corresponding to the authenticity grading data in a preset business strategy database;
The second data acquisition module is used for acquiring transaction big data of the user to be evaluated, wherein the transaction big data are transaction data of the user to be evaluated in an official institution;
The transaction analysis module is used for screening the transaction big data based on a preset data screening rule to obtain transaction analysis data;
The preprocessing module is used for preprocessing the stream analysis data and the transaction analysis data to obtain verification characteristic data;
the verification scoring module is used for carrying out verification scoring processing on the verification characteristic data based on a preset verification model to obtain the authenticity scoring data;
preprocessing the stream analysis data and the transaction analysis data to obtain verification feature data, wherein the preprocessing comprises the following steps:
based on the transaction analysis data, carrying out cross comparison on the stream analysis data to obtain comparison result data;
according to a preset time period, selecting transaction analysis data in the preset time period to be compared with stream analysis data in a poor way, and obtaining a comparison result;
If the comparison result data meets the preset condition, integrating the flow analysis data and the transaction analysis data to obtain the verification feature data, and if the comparison result data is smaller than the preset difference condition, taking the flow analysis data and the transaction analysis data as basic dimensions, modeling through big data, combining the basic dimensions such as time, space and transaction type, classifying and integrating the flow analysis data and the transaction analysis data, and on the basis, deriving feature variables to perfect the whole data dimension to obtain the verification feature data;
if the comparison result data does not meet the preset condition, that is, the comparison result is larger than the preset difference condition, judging that the flow analysis data is false data, marking the flow analysis data, processing the transaction analysis data to obtain transaction characteristic data, recording the judgment result data, and deriving characteristic variables of the transaction analysis data to obtain the transaction characteristic data.
6. A computer readable storage medium storing computer instructions for causing the computer to perform the data processing method for anti-fraud of any of claims 1-4.
7. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to cause the at least one processor to perform the data processing method for anti-fraud of any of claims 1-4.
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| CN109784933A (en) * | 2019-01-23 | 2019-05-21 | 集奥聚合(北京)人工智能科技有限公司 | A kind of anti-fraud rule model building system and method based on data variable |
| CN109886801A (en) * | 2019-01-28 | 2019-06-14 | 上海孚厘金融信息服务有限公司 | A kind of e-bank's flowing water distinguishing method between true and false and device |
| CN110414914A (en) * | 2019-06-17 | 2019-11-05 | 深圳壹账通智能科技有限公司 | Business datum monitoring method and device |
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