Cross-bank transaction processing method and apparatus
By generating customer authentication fingerprints and performing biometric matching in inter-bank transactions, the security issues of inter-bank transactions are resolved, transaction risks are reduced, and the smooth execution of transactions and the protection of customer information are ensured.
Patent Information
- Application Number
- CN202210593376.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-05-27
- Publication Date
- 2025-10-24
- Estimated Expiration
- 2042-05-27
AI Technical Summary
When conducting inter-bank transactions, there is a high risk of customer information leakage and transaction risks are difficult to control. Existing technologies cannot effectively guarantee the security of inter-bank transactions.
Generate customer authentication fingerprints for each customer through the first bank, match biometric information with that of the second bank, and use biometric information to process transactions and reduce risks.
It has improved the security of inter-bank transactions, reduced transaction risks, protected customer information, and ensured the smooth execution of transactions.
Smart Images
Figure CN114936934B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data processing, and particularly relates to a cross-bank transaction processing method and device. BACKGROUND
[0002] This section is intended to provide background information to facilitate an understanding of embodiments of the application as set forth in the claims. The description herein does not constitute an admission of prior art.
[0003] With the continuous progress of science and technology and living conditions, people's living range is getting larger and larger, and they are no longer limited to living in a certain area, resulting in that the demand of bank customers for cross-bank transactions is getting larger and larger. Cross-bank transactions are an indispensable part of most customers' lives.
[0004] When performing cross-bank transactions, customers need to input important customer information such as account numbers or mobile phone numbers on other bank devices, which may lead to leakage of important customer information, and because the bank devices belong to other banks and lack sufficient data, the risk of cross-bank transactions of customers cannot be controlled.
[0005] In view of the above, there is an urgent need for a technical solution that can overcome the above-mentioned defects, improve the security of cross-bank transactions, and reduce the risk of cross-bank transactions. SUMMARY
[0006] To solve the problems existing in the prior art, the present application provides a cross-bank transaction processing method and device.
[0007] In a first aspect of the embodiments of the present application, a cross-bank transaction processing method is provided, comprising:
[0008] The first bank generates a customer authentication fingerprint corresponding to each customer;
[0009] When the second bank obtains the customer authentication fingerprint of the first bank input by the current customer, the customer authentication fingerprint is sent to the first bank;
[0010] The first bank determines whether the customer authentication fingerprint is the customer authentication fingerprint of the current customer, and if so, sends the biological feature information corresponding to the customer authentication fingerprint to the second bank;
[0011] When the second bank obtains the biological feature information input by the current customer, the biological feature information corresponding to the customer authentication fingerprint is matched with the biological feature information input by the current customer, and the transaction of the current customer is processed according to the biological feature matching result.
[0012] In a second aspect of the embodiments of the present application, a cross-bank transaction processing device is provided, comprising:
[0013] The generating module is arranged in the first bank and is configured to generate a customer authentication fingerprint corresponding to each customer;
[0014] The acquiring module is arranged in the second bank and is configured to send the customer authentication fingerprint input by the current customer to the first bank when the customer authentication fingerprint is acquired;
[0015] The judging module is arranged in the first bank and is configured to judge whether the customer authentication fingerprint is the customer authentication fingerprint of the current customer;
[0016] The sending module is arranged in the first bank and is configured to send the biological feature information corresponding to the customer authentication fingerprint to the second bank when the customer authentication fingerprint is the customer authentication fingerprint of the current customer;
[0017] The processing module is arranged in the second bank and is configured to match the biological feature information corresponding to the customer authentication fingerprint with the biological feature information input by the current customer when the biological feature information input by the current customer is acquired, and to process the transaction of the current customer according to the biological feature matching result.
[0018] In a third aspect of the embodiments of the present application, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the cross-bank transaction processing method when executing the computer program.
[0019] In a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the cross-bank transaction processing method when executed by a processor.
[0020] In a fifth aspect of the embodiments of the present application, a computer program product is provided, which includes a computer program, and the computer program implements the cross-bank transaction processing method when executed by a processor.
[0021] The cross-bank transaction processing method and device generate a customer authentication fingerprint corresponding to each customer by a first bank; when the second bank obtains a customer authentication fingerprint of the first bank input by a current customer, the second bank sends the customer authentication fingerprint to the first bank; the first bank judges whether the customer authentication fingerprint is the customer authentication fingerprint of the current customer, and if so, sends biological feature information corresponding to the customer authentication fingerprint to the second bank; when the second bank obtains biological feature information input by the current customer, the second bank matches the biological feature information corresponding to the customer authentication fingerprint with the biological feature information input by the current customer, and processes the transaction of the current customer according to the biological feature matching result. The overall scheme can make the risk of cross-bank transaction controllable from the aspects of customer authentication fingerprint and identity feature recognition threshold of a transaction location, reduce the risk of cross-bank transaction, effectively protect the information of the customer, and make the cross-bank transaction processing smoothly executed. BRIEF DESCRIPTION OF DRAWINGS
[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0023] Figure 1 is a cross-bank transaction processing method flowchart of an embodiment of the present application.
[0024] Figure 2 is a process flowchart of generating a customer authentication fingerprint corresponding to each customer of an embodiment of the present application.
[0025] Figure 3 is a process flowchart of obtaining a customer category of an embodiment of the present application.
[0026] Figure 4 is a process flowchart of determining a biological feature recognition threshold by analyzing a transaction location of an embodiment of the present application.
[0027] Figure 5 is a process flowchart of determining a bank self-service terminal corresponding to the current transaction of the current customer of an embodiment of the present application.
[0028] Figure 6 is a process flowchart of determining a biological feature recognition threshold corresponding to the current transaction of the current customer of an embodiment of the present application.
[0029] Figure 7 is a cross-bank transaction processing device architecture diagram of an embodiment of the present application.
[0030] Figure 8 is a cross-bank transaction processing device architecture diagram of another embodiment of the present application.
[0031] Figure 9 is a schematic diagram of a computer device structure according to an embodiment of the present application. DETAILED DESCRIPTION
[0032] The principles and spirits of the present application will be described below with reference to several exemplary embodiments. It should be understood that the embodiments are given only so that those skilled in the art can better understand and implement the present application, and are not intended to limit the scope of the present application in any way. On the contrary, the embodiments are provided so that the present disclosure is more thorough and complete, and the scope of the present disclosure is fully conveyed to those skilled in the art.
[0033] Those skilled in the art will appreciate that the embodiments of the present application can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure can be embodied in the form of an entirely hardware, an entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.
[0034] According to the embodiments of the present application, a cross-bank transaction processing method and device are provided, relating to the technical field of data processing.
[0035] The principles and spirits of the present application will be described below with reference to several exemplary embodiments. It should be understood that the embodiments are given only so that those skilled in the art can better understand and implement the present application, and are not intended to limit the scope of the present application in any way. On the contrary, the embodiments are provided so that the present disclosure is more thorough and complete, and the scope of the present disclosure is fully conveyed to those skilled in the art.
[0036] Figure 1 is a schematic diagram of a cross-bank transaction processing method according to an embodiment of the present application. As shown in Figure 1 , the method comprises:
[0037] S1, the first bank generates a customer authentication fingerprint corresponding to each customer;
[0038] S2, when the second bank obtains the customer authentication fingerprint of the first bank input by the current customer, the customer authentication fingerprint is sent to the first bank;
[0039] In actual application scenarios, the way of inputting the customer authentication fingerprint can be that the bank system scans the mobile terminal of the customer, or manual input, or other input methods.
[0040] S3, the first bank determines whether the customer authentication fingerprint is the customer authentication fingerprint of the current customer, and if so, sends the biometric information corresponding to the customer authentication fingerprint to the second bank;
[0041] S4, when the second bank obtains the biometric information input by the current customer, matches the biometric information corresponding to the customer authentication fingerprint with the biometric information input by the current customer, and processes the transaction of the current customer according to the biometric matching result.
[0042] The acquisition, storage, use, processing, etc. of the biometric feature related data in the technical solutions of the present application comply with relevant provisions of national laws and regulations.
[0043] To more clearly explain the above cross-bank transaction processing method, the following will be described in detail in combination with each step.
[0044] In S1, referring to Figure 2 , the specific method for the first bank to generate the customer authentication fingerprint corresponding to each customer is as follows:
[0045] S101, determining the risk indicators of each customer category with respect to each transaction category;
[0046] S102, for each customer, determining the multiple transaction categories corresponding to the customer according to the transaction data of the customer;
[0047] S103, determining the selected coefficients of each transaction category corresponding to the customer according to the customer category to which the customer belongs and the risk indicators of each customer category with respect to each transaction category;
[0048] S104, determining the first transaction category corresponding to the customer according to the selected coefficients;
[0049] S105, generating the customer authentication fingerprint corresponding to the customer based on the first transaction category corresponding to the customer.
[0050] In an embodiment, (S101) determining the risk indicators of each customer category with respect to each transaction category comprises:
[0051] S1011, acquiring the transaction data of each customer category with respect to each transaction category;
[0052] S1012, dividing the transaction data into multiple transaction data subsets in chronological order, wherein the number of transactions included in each transaction data subset is greater than a first threshold, and the difference between the number of transactions included in any two transaction data subsets is less than a second threshold;
[0053] S1013, taking the proportion of the transaction data involving risks in each transaction data subset in the multiple transaction data subsets as the risk indicator sample of the customer category with respect to the transaction category;
[0054] S1014, determining the risk indicator of the customer category with respect to the transaction category as the mean of the risk indicator samples of the customer category with respect to the transaction category.
[0055] More specifically, the specific method for (S101) determining the risk indicators of each customer category with respect to each transaction category is as follows:
[0056] After obtaining the number of transaction data involving risk in each of the plurality of transaction data subsets as the risk indicator sample of the customer category with respect to the transaction category, the first bank determines the sample variance corresponding to the risk indicator of the customer category with respect to the transaction category and the corresponding sample number based on all the risk indicator samples of the customer category with respect to the transaction category obtained;
[0057] The quotient of the square of the sample variance corresponding to the risk indicator of the customer category with respect to the transaction category and the corresponding sample number is taken as the error convergence value;
[0058] The acceptable risk indicator error maximum value and the probability that the acceptable risk indicator error is greater than the risk indicator error maximum value are set;
[0059] The product of the square of the risk indicator error maximum value and the probability is taken as the error threshold value;
[0060] The size relationship between the error convergence value and the error threshold value is determined;
[0061] If the error convergence value is greater than the error threshold value, the following steps are repeatedly performed until the error convergence value is less than or equal to the error threshold value:
[0062] The first bank obtains new transaction data of the customer category with respect to the transaction category;
[0063] The new transaction data is divided into a plurality of new transaction data subsets in chronological order, wherein the number of transactions contained in each new transaction data subset is greater than a first threshold value, and the difference between the number of transactions contained in any two transaction data subsets is less than a second threshold value;
[0064] The number of transaction data involving risk in each of the plurality of new transaction data subsets is taken as the risk indicator sample of the customer category with respect to the transaction category;
[0065] The sample variance corresponding to the risk indicator of the customer category with respect to the transaction category and the corresponding sample number are updated based on all the risk indicator samples of the customer category with respect to the transaction category obtained;
[0066] The error convergence value is updated to the quotient of the square of the sample variance corresponding to the risk indicator of the customer category with respect to the transaction category and the corresponding sample number;
[0067] When the error convergence value is less than or equal to the error threshold value, the risk indicator of the customer category with respect to the transaction category is determined as the mean of the risk indicator sample of the customer category with respect to the transaction category.
[0068] In an embodiment, with reference to Figure 3 The customer category can be obtained in the following manner:
[0069] S01, determining a distance function of the customer as the square root of the sum of squares of functions corresponding to all dimensions, wherein each dimension corresponds to a bank transaction one by one, and for any dimension, the argument of the function corresponding to the dimension is two customers, and the function value corresponding to the dimension is the difference between the transaction quantities of the two customers in the bank transaction corresponding to the dimension;
[0070] S02, selecting a clustering algorithm according to the distance function of the customer to cluster the bank customers in a predetermined area to obtain a plurality of customer subsets;
[0071] S03, for each customer subset obtained, determining the main transaction of each customer in the customer subset (that is, the transaction with the largest transaction data among all transactions of the customer). From all the main transactions, the main transaction corresponding to the customer subset is selected, and the number of customers of the customer subset corresponding to the selected main transaction is determined as the concentration index of the customer subset;
[0072] S04, for each customer subset obtained, determining whether the following condition t is met: the ratio of the concentration index of the customer subset to the number of customers of the customer subset is greater than a set value, if not, continue to cluster the customer subset using the clustering algorithm until each newly generated customer subset meets the condition t;
[0073] S05, determining each finally determined customer subset as a customer category.
[0074] In an embodiment, (S103) determining the selected coefficient of each transaction category corresponding to the customer according to the customer category to which the customer belongs and the risk index of each customer category with respect to each transaction category, comprising:
[0075] For each transaction category corresponding to the customer, determining the security coefficient corresponding to the transaction category according to the risk index of the customer category to which the customer belongs with respect to the transaction category;
[0076] For example, the security coefficient is determined as the reciprocal of the corresponding risk index, or the difference between 1 and the corresponding risk index.
[0077] The selected coefficient of each transaction category corresponding to the customer is determined according to the following calculation formula:
[0078]
[0079] Wherein, p i is the selected coefficient of the i-th transaction category corresponding to the customer, s j is the security coefficient corresponding to the j-th transaction category corresponding to the customer.
[0080] In an embodiment, (S104) determining the first transaction category corresponding to the customer according to the selected coefficients comprises:
[0081] selecting a probability density function g, and determining a minimum value m of a domain of the probability density function g;
[0082] sorting the multiple transaction categories corresponding to the customer;
[0083] for each transaction category corresponding to the customer, determining an endpoint value corresponding to the transaction category, wherein an integral value of the probability density function g from the minimum value m to the endpoint value corresponding to the transaction category is equal to a sum of the selected coefficients of all transaction categories before the transaction category in the sorting;
[0084] generating a random number according to the probability density function g, and selecting a maximum endpoint value smaller than the random number from the endpoint values corresponding to the multiple transaction categories corresponding to the customer, and taking the transaction category corresponding to the maximum endpoint value as the first transaction category corresponding to the customer.
[0085] In an embodiment, (S105) generating the customer authentication fingerprint corresponding to the customer based on the first transaction category corresponding to the customer can be selecting transaction data corresponding to the first transaction category from the transaction data of the customer, and taking a hash value of the selected transaction data (or a part of the transaction data) as the customer authentication fingerprint corresponding to the customer.
[0086] Further, referring to Figure 4 The method further comprises:
[0087] S5, the first bank acquires a transaction location of the current transaction of the current customer;
[0088] S6, determining a bank self-service terminal corresponding to the current transaction of the current customer according to the transaction location;
[0089] S7, determining a biometric recognition threshold corresponding to the current transaction of the current customer according to the bank self-service terminal corresponding to the current transaction of the current customer;
[0090] S8, sending the biometric recognition threshold to the second bank, wherein the second bank performs biometric recognition on the current customer according to the biometric recognition threshold.
[0091] In an embodiment, referring to Figure 5 (S6) determining the bank self-service terminal corresponding to the current transaction of the current customer according to the transaction location comprises:
[0092] S601, acquiring multiple payment locations of the transaction location of the current transaction of the current customer corresponding to the first bank;
[0093] In an actual application scenario, the payment location (mentioned in S601) is a transaction location of a transaction counterparty near the transaction location in the payment data of the first bank. The transaction counterparty can be a restaurant, a supermarket, etc.
[0094] For example, when a customer uses the account of the first bank to transact at a self-service terminal of the second bank located at location A, the payment location obtained is a payment location belonging to the first bank and located near (e.g., 50 m) location A.
[0095] Since the self-service terminal belongs to the second bank, the first bank cannot obtain the customer data of the self-service terminal. To this end, this step is based on the location (location A) of the self-service terminal to obtain a plurality of payment locations belonging to the first bank near location A.
[0096] S602, obtaining a customer set corresponding to the payment data of the plurality of payment locations;
[0097] S603, determining an environmental customer vector corresponding to the current transaction of the current customer according to the customer set;
[0098] S604, determining a customer vector of each self-service terminal according to the transaction data of each self-service terminal of the first bank;
[0099] S605, determining a bank self-service terminal corresponding to the current transaction of the current customer according to the environmental customer vector and the customer vector.
[0100] Specifically, one method for determining the environmental customer vector corresponding to the current transaction of the current customer according to the customer set (S603) is as follows:
[0101] Determine the environmental customer vector corresponding to the current transaction of the current customer, wherein the components of the environmental customer vector correspond to the customer categories one by one, and the value of each component is equal to the number of customers belonging to the customer category corresponding to the component in the customer set.
[0102] Specifically, one method for determining the customer vector of each self-service terminal of the first bank according to the transaction data of each self-service terminal of the first bank (S604) is as follows:
[0103] For each self-service terminal of the first bank, determine the number of customers belonging to each customer category among all customers corresponding to the transaction data of the self-service terminal;
[0104] Determine the customer vector of each self-service terminal of the first bank, wherein the components of the customer vector correspond to the customer categories one by one, and the value of each component is equal to the number of customers belonging to the customer category corresponding to the component among all customers corresponding to the transaction data of the self-service terminal.
[0105] Specifically, (S605) according to the environment customer vector and the customer vector, a method for determining the bank self-service terminal corresponding to the current transaction of the current customer is as follows:
[0106] For each self-service terminal of the first bank, the distance between the customer vector of the self-service terminal and the environment customer vector corresponding to the current transaction of the current customer is taken as the distance corresponding to the self-service terminal;
[0107] From all the self-service terminals of the first bank, the self-service terminal with the minimum corresponding distance is taken as the bank self-service terminal corresponding to the current transaction of the current customer.
[0108] It should be noted that the distance of the vector includes the Euclidean distance, the cosine distance, etc.
[0109] Specifically, (S605) according to the environment customer vector and the customer vector, another method for determining the bank self-service terminal corresponding to the current transaction of the current customer is as follows:
[0110] For each self-service terminal of the first bank, the difference between the customer vector of the self-service terminal and the environment customer vector corresponding to the current transaction of the current customer is taken as the customer vector difference corresponding to the self-service terminal;
[0111] The partial order of the self-service terminals is determined, wherein for any two self-service terminals of the first bank, if for each component, the absolute value of the value of the customer vector difference corresponding to the first self-service terminal of the two self-service terminals at the component is less than or equal to the absolute value of the value of the customer vector difference corresponding to the second self-service terminal of the two self-service terminals at the component, it is determined that the first self-service terminal is superior to the second self-service terminal;
[0112] According to the partial order of the self-service terminals, the greatest element of the partial order is selected from all the self-service terminals of the first bank;
[0113] The greatest element of the partial order selected is taken as the bank self-service terminal corresponding to the current transaction of the current customer.
[0114] Specifically, (S605) according to the environment customer vector and the customer vector, another method for determining the bank self-service terminal corresponding to the current transaction of the current customer is as follows:
[0115] According to the payment data of the plurality of payment locations corresponding to the transaction location of the current transaction of the current customer of the first bank, the payment data amount corresponding to the current transaction of the current customer is determined;
[0116] For each self-service terminal of the first bank, the payment data of the plurality of payment locations corresponding to the self-service terminal of the first bank is obtained, and the payment data amount corresponding to the self-service terminal is determined according to the payment data;
[0117] For each self-service terminal of the first bank, a distance between a customer vector of the self-service terminal and an environmental customer vector corresponding to the current customer's current transaction is determined as a customer distance corresponding to the self-service terminal, and an absolute value of a difference between a payment data amount corresponding to the self-service terminal and a payment data amount corresponding to the current customer's current transaction is determined as a payment amount distance corresponding to the self-service terminal;
[0118] A partial order of the self-service terminals is determined, wherein for any two self-service terminals of the first bank, if a customer distance corresponding to a first self-service terminal of the two self-service terminals is less than or equal to a customer distance corresponding to a second self-service terminal of the two self-service terminals, and a payment amount distance corresponding to the first self-service terminal is less than or equal to a payment amount distance corresponding to the second self-service terminal, it is determined that the first self-service terminal is superior to the second self-service terminal;
[0119] According to the partial order of the self-service terminals, a maximal element of the partial order is selected from all self-service terminals of the first bank;
[0120] The selected maximal element of the partial order is determined as the bank self-service terminal corresponding to the current customer's current transaction.
[0121] It should be noted that the maximal element of the partial order is an element that is not superior to any other element in the set corresponding to the partial order.
[0122] In an embodiment, with reference to Figure 6 , (S7) determining a biometric recognition threshold corresponding to the current customer's current transaction according to the bank self-service terminal corresponding to the current customer's current transaction, comprising:
[0123] S701, determining a corresponding relationship between a biometric recognition threshold and a risk index according to the biometric recognition data stored in the server of the first bank, wherein for each biometric recognition threshold, when a preset matching threshold is set to the biometric recognition threshold, a proportion of biometric recognition data involving risks in the biometric recognition data stored in the server of the first bank is determined, and the proportion is taken as the risk index corresponding to the biometric recognition threshold;
[0124] S702, determining a risk index corresponding to each customer according to the biometric recognition threshold corresponding to each customer stored in the server of the first bank;
[0125] S703, for each bank self-service terminal corresponding to the current transaction of the current customer, determining the corresponding relationship between the biometric identification threshold and the risk index of the bank self-service terminal according to the biometric identification data of the bank self-service terminal, wherein for each biometric identification threshold, when the preset matching threshold is set to the biometric identification threshold, the proportion of biometric identification data related to risk in the biometric identification data of the bank self-service terminal is determined, and the proportion is taken as the risk index corresponding to the biometric identification threshold.
[0126] S704, according to the risk index corresponding to the current customer, and the corresponding relationship between the biometric identification threshold and the risk index of each bank self-service terminal corresponding to the current transaction of the current customer, determining the biometric identification threshold corresponding to the current transaction of the current customer.
[0127] Specifically, (S704) according to the risk index corresponding to the current customer, and the corresponding relationship between the biometric identification threshold and the risk index of each bank self-service terminal corresponding to the current transaction of the current customer, determining the biometric identification threshold corresponding to the current transaction of the current customer, comprising:
[0128] For each bank self-service terminal corresponding to the current transaction of the current customer, according to the risk index corresponding to the customer, and the corresponding relationship between the biometric identification threshold and the risk index of the bank self-service terminal, determining the biometric identification threshold of the current transaction of the current customer with respect to the bank self-service terminal;
[0129] The biometric identification threshold corresponding to the current transaction of the current customer is determined as the maximum value of the biometric identification threshold of the current transaction of the current customer with respect to each corresponding bank self-service terminal.
[0130] It should be noted that although the operations of the method of the present application are described in a specific order in the above embodiments and drawings, this does not require or imply that the operations must be performed in this specific order, or that all of the shown operations must be performed to achieve the desired result. Additionally or alternatively, certain steps can be omitted, a plurality of steps can be combined into one step, and / or one step can be divided into a plurality of steps.
[0131] After introducing the method of the exemplary embodiment of the present application, next, with reference to the accompanying drawings, Figure 7 The cross-bank transaction processing device of the exemplary embodiment of the present application is introduced.
[0132] The implementation of the cross-bank transaction processing apparatus can refer to the implementation of the above method, and the repeated parts will not be described herein. The term "module" or "unit" used below can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementation of hardware or a combination of software and hardware is also possible and contemplated.
[0133] Based on the same inventive concept, the present application further provides a cross-bank transaction processing apparatus, as shown in the accompanying drawings, which comprises: Figure 7
[0134] The generating module 110 is arranged in the first bank 100 and is configured to generate a customer authentication fingerprint corresponding to each customer;
[0135] The acquiring module 210 is arranged in the second bank 200 and is configured to, when acquiring the customer authentication fingerprint of the first bank input by the current customer, send the customer authentication fingerprint to the first bank;
[0136] The determining module 120 is arranged in the first bank and is configured to determine whether the customer authentication fingerprint is the customer authentication fingerprint of the current customer;
[0137] The sending module 130 is arranged in the first bank and is configured to, when the customer authentication fingerprint is the customer authentication fingerprint of the current customer, send the biological feature information corresponding to the customer authentication fingerprint to the second bank;
[0138] The processing module 220 is arranged in the second bank and is configured to, when acquiring the biological feature information input by the current customer, match the biological feature information corresponding to the customer authentication fingerprint with the biological feature information input by the current customer, and process the transaction of the current customer according to the biological feature matching result.
[0139] In an embodiment, the generating module is specifically configured to:
[0140] determine the risk indicators of each customer category with respect to each transaction category;
[0141] for each customer, determine a plurality of transaction categories corresponding to the customer according to the transaction data of the customer;
[0142] determine the selected coefficients of each transaction category corresponding to the customer according to the customer category to which the customer belongs and the risk indicators of each customer category with respect to each transaction category;
[0143] determine the first transaction category corresponding to the customer according to the selected coefficients;
[0144] generate the customer authentication fingerprint corresponding to the customer based on the first transaction category corresponding to the customer.
[0145] In an embodiment, the generating module is specifically configured to:
[0146] obtain transaction data of each customer category with respect to each transaction category;
[0147] divide the transaction data into a plurality of transaction data subsets in chronological order, wherein each transaction data subset contains a number of transactions greater than a first threshold, and the difference between the number of transactions contained in any two transaction data subsets is less than a second threshold;
[0148] determine the proportion of the number of transaction data involving risks in each transaction data subset in the plurality of transaction data subsets as a risk indicator sample of the customer category with respect to the transaction category;
[0149] determine the risk indicator of the customer category with respect to the transaction category as the average of the risk indicator samples of the customer category with respect to the transaction category.
[0150] In an embodiment, the generating module is specifically configured to:
[0151] for each transaction category corresponding to the customer, determine a security coefficient corresponding to the transaction category according to the risk indicator of the customer category to which the customer belongs with respect to the transaction category;
[0152] determine the selected coefficient of each transaction category corresponding to the customer according to the following calculation formula:
[0153]
[0154] wherein p i is the selected coefficient of the i-th transaction category corresponding to the customer, s j is the security coefficient corresponding to the j-th transaction category corresponding to the customer.
[0155] In an embodiment, the generating module is specifically configured to:
[0156] select a probability density function g and determine the minimum value m of the domain of the probability density function g;
[0157] sort the plurality of transaction categories corresponding to the customer;
[0158] for each transaction category corresponding to the customer, determine an endpoint value corresponding to the transaction category, wherein the integral value of the probability density function g from the minimum value m to the endpoint value corresponding to the transaction category is equal to the sum of the selected coefficients of all transaction categories before the transaction category in the sorting;
[0159] According to the probability density function g, a random number is generated, and the maximum endpoint value smaller than the random number is selected from the endpoint values corresponding to the multiple transaction categories of the customer, and the transaction category corresponding to the maximum endpoint value is taken as the first transaction category corresponding to the customer.
[0160] In an embodiment, the analysis module is configured to: Figure 8 The apparatus further comprises an analysis module 140, which is arranged in the first bank, and is configured to:
[0161] The analysis module is specifically configured to:
[0162] Obtain the transaction location of the current transaction of the current customer;
[0163] Determine the bank self-service terminal corresponding to the current transaction of the current customer according to the transaction location;
[0164] Determine the biometric recognition threshold corresponding to the current transaction of the current customer according to the bank self-service terminal corresponding to the current transaction of the current customer;
[0165] Send the biometric recognition threshold to the second bank, wherein the second bank performs biometric recognition on the current customer according to the biometric recognition threshold.
[0166] In an embodiment, the analysis module is specifically configured to:
[0167] Obtain multiple payment locations of the transaction location of the current transaction of the current customer corresponding to the first bank;
[0168] Obtain a customer set corresponding to the payment data of the multiple payment locations;
[0169] Determine the environmental customer vector corresponding to the current transaction of the current customer according to the customer set;
[0170] Determine the customer vector of each self-service terminal according to the transaction data of each self-service terminal of the first bank;
[0171] Determine the bank self-service terminal corresponding to the current transaction of the current customer according to the environmental customer vector and the customer vector.
[0172] In an embodiment, the analysis module is specifically configured to:
[0173] Determine the corresponding relationship between the biometric recognition threshold and the risk index according to the biometric recognition data stored in the server of the first bank, wherein for each biometric recognition threshold, when a preset matching threshold is set to the biometric recognition threshold, a proportion of biometric recognition data involving risks in the biometric recognition data stored in the server of the first bank is determined, and the proportion is taken as the risk index corresponding to the biometric recognition threshold;
[0174] According to the biometric threshold value corresponding to each customer stored by the server of the first bank, determine the risk index corresponding to each customer;
[0175] For each bank self-service terminal corresponding to the current transaction of the current customer, according to the biometric data of the bank self-service terminal, determine the corresponding relationship between the biometric threshold value and the risk index of the bank self-service terminal, wherein for each biometric threshold value, when the preset matching threshold value is set to the biometric threshold value, determine the proportion of biometric data involving risk in the biometric data of the bank self-service terminal, and take the proportion as the risk index corresponding to the biometric threshold value;
[0176] According to the risk index corresponding to the current customer, and the corresponding relationship between the biometric threshold value and the risk index of each bank self-service terminal corresponding to the current transaction of the current customer, determine the biometric threshold value corresponding to the current transaction of the current customer.
[0177] It should be noted that although several modules of the cross-bank transaction processing apparatus are mentioned in the foregoing detailed description, such division is merely exemplary and not mandatory. Indeed, according to embodiments of the application, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided into modules.
[0178] Based on the foregoing inventive concept, as Figure 9 The present application further proposes a computer device 900, comprising a memory 910, a processor 920, and a computer program 930 stored in the memory 910 and executable on the processor 920, wherein the processor 920 implements the foregoing cross-bank transaction processing method when executing the computer program 930.
[0179] Based on the foregoing inventive concept, the present application proposes a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the foregoing cross-bank transaction processing method.
[0180] Based on the foregoing inventive concept, the present application proposes a computer program product, which comprises a computer program, wherein the computer program is executed by a processor to implement the cross-bank transaction processing method.
[0181] The cross-bank transaction processing method and device generate a customer authentication fingerprint corresponding to each customer by a first bank; when a second bank obtains a customer authentication fingerprint of the first bank input by a current customer, the second bank sends the customer authentication fingerprint to the first bank; the first bank judges whether the customer authentication fingerprint is the customer authentication fingerprint of the current customer, and if so, sends biological feature information corresponding to the customer authentication fingerprint to the second bank; when the second bank obtains biological feature information input by the current customer, the second bank matches the biological feature information corresponding to the customer authentication fingerprint with the biological feature information input by the current customer, and processes a transaction of the current customer according to a biological feature matching result. The overall scheme can make the risk of cross-bank transaction controllable from aspects of a customer authentication fingerprint and an identity feature recognition threshold of a transaction location, reduce the risk of cross-bank transaction, effectively protect information of the customer, and make cross-bank transaction processing smoothly executed.
[0182] Those skilled in the art will understand that embodiments of the present application can be provided as methods, apparatuses, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.
[0183] The present application is described with reference to flowcharts and / or block diagrams of methods and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce an apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 An apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 An apparatus that implements the functions specified in the flowcharts and / or block diagrams.
[0184] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatuses that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 An apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 An apparatus that implements the functions specified in the flowcharts and / or block diagrams.
[0185] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1
[0186] Finally, it should be noted that the above-described embodiments are merely exemplary of the application and should not be used to limit its scope, and that the scope of the application is defined by the appended claims. Although the application has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations as fall within the scope of the claims appended hereto.
Claims
1. A cross-bank transaction processing method, characterized by, The method comprises: The first bank generates a customer authentication fingerprint corresponding to each customer; When the second bank obtains the customer authentication fingerprint of the first bank input by the current customer, the second bank sends the customer authentication fingerprint to the first bank; The first bank determines whether the customer authentication fingerprint is the customer authentication fingerprint of the current customer, and if so, sends the biological feature information corresponding to the customer authentication fingerprint to the second bank; When the second bank obtains the biological feature information input by the current customer, the second bank matches the biological feature information corresponding to the customer authentication fingerprint with the biological feature information input by the current customer, and processes the transaction of the current customer according to the biological feature matching result; The method further comprises: The first bank obtains the transaction location of the current transaction of the current customer; According to the transaction location, the bank self-service terminal corresponding to the current transaction of the current customer is determined; According to the bank self-service terminal corresponding to the current transaction of the current customer, the biological feature recognition threshold corresponding to the current transaction of the current customer is determined; The biological feature recognition threshold is sent to the second bank, wherein the second bank performs biological feature recognition on the current customer according to the biological feature recognition threshold; According to the transaction location, the bank self-service terminal corresponding to the current transaction of the current customer is determined, which comprises: Obtain a plurality of payment locations of the transaction location of the current transaction of the current customer corresponding to the first bank; wherein the payment location is the transaction location of the transaction counterparty located near the transaction location in the payment data of the first bank; Obtain the customer set corresponding to the payment data of the plurality of payment locations; According to the customer set, the environmental customer vector corresponding to the current transaction of the current customer is determined; According to the transaction data of each self-service terminal of the first bank, the customer vector of each self-service terminal is determined; According to the environmental customer vector and the customer vector, the bank self-service terminal corresponding to the current transaction of the current customer is determined; According to the transaction location, the bank self-service terminal corresponding to the current transaction of the current customer is determined, which comprises: According to the biological feature recognition data stored in the server of the first bank, the corresponding relationship between the biological feature recognition threshold and the risk index is determined, wherein for each biological feature recognition threshold, when the preset matching threshold is set to the biological feature recognition threshold, the proportion of the biological feature recognition data involving risk in the biological feature recognition data stored in the server of the first bank is determined, and the proportion is taken as the risk index corresponding to the biological feature recognition threshold; According to the biological feature recognition threshold corresponding to each customer stored in the server of the first bank, the risk index corresponding to each customer is determined; For each bank self-service terminal corresponding to the current transaction of the current customer, the corresponding relationship between the biological feature recognition threshold and the risk index of the bank self-service terminal is determined according to the biological feature recognition data of the bank self-service terminal, wherein for each biological feature recognition threshold, when the preset matching threshold is set to the biological feature recognition threshold, the proportion of the biological feature recognition data involving risk in the biological feature recognition data of the bank self-service terminal is determined, and the proportion is taken as the risk index corresponding to the biological feature recognition threshold; According to the risk index corresponding to the current customer and the corresponding relationship between the biometric identification threshold of each bank self-service terminal corresponding to the current transaction of the current customer and the risk index, a biometric identification threshold corresponding to the current transaction of the current customer is determined.
2. The method of claim 1, wherein, The first bank generates a customer authentication fingerprint corresponding to each customer, including: determining the risk index of each customer category with respect to each transaction category; For each customer, according to the transaction data of the customer, a plurality of transaction categories corresponding to the customer are determined; According to the customer category to which the customer belongs and the risk index of each customer category with respect to each transaction category, a selected coefficient of each transaction category corresponding to the customer is determined; According to the selected coefficient, a first transaction category corresponding to the customer is determined; Based on the first transaction category corresponding to the customer, a customer authentication fingerprint corresponding to the customer is generated.
3. The method of claim 1, wherein, determining the risk index of each customer category with respect to each transaction category, including: obtaining transaction data of each customer category with respect to each transaction category; Divide the transaction data into a plurality of transaction data subsets in chronological order, wherein the number of transactions contained in each transaction data subset is greater than a first threshold, and the difference between the number of transactions contained in any two transaction data subsets is less than a second threshold; The proportion of the number of risk-related transaction data in each transaction data subset in the plurality of transaction data subsets is taken as a risk index sample of the customer category with respect to the transaction category; The risk index of the customer category with respect to the transaction category is determined as the mean of the risk index sample of the customer category with respect to the transaction category.
4. The method of claim 1, wherein, According to the customer category to which the customer belongs and the risk index of each customer category with respect to each transaction category, a selected coefficient of each transaction category corresponding to the customer is determined, including: For each transaction category corresponding to the customer, according to the risk index of the customer category to which the customer belongs with respect to the transaction category, a security coefficient corresponding to the transaction category is determined; The selected coefficient of each transaction category corresponding to the customer is determined according to the following calculation formula: Among them, p i is the selected coefficient of the i-th transaction category corresponding to the customer, s j The safety factor corresponding to the j-th transaction category corresponding to the customer.
5. The method of claim 1, wherein, According to the selected coefficient, a first transaction category corresponding to the customer is determined, including: selecting a probability density function g and determining the minimum value m of the domain of the probability density function g; Sort the plurality of transaction categories corresponding to the customer; For each transaction category corresponding to the customer, determine an endpoint value corresponding to the transaction category, wherein the integral value of the probability density function g from the minimum value m to the endpoint value corresponding to the transaction category is equal to the sum of the selected coefficients of all transaction categories before the transaction category in the sorting; According to the probability density function g, a random number is generated, and the largest endpoint value smaller than the random number is selected from the endpoint values corresponding to the plurality of transaction categories corresponding to the customer, and the transaction category corresponding to the largest endpoint value is taken as the first transaction category corresponding to the customer.
6. A cross-bank transaction processing apparatus characterized by comprising: including: a generation module arranged in the first bank for generating a customer authentication fingerprint corresponding to each customer; an acquisition module arranged in the second bank for sending the customer authentication fingerprint input by the current customer to the first bank when the customer authentication fingerprint of the first bank is acquired. The device further comprises an analysis module arranged in the first bank, wherein the analysis module is configured to: obtain a transaction location of the current transaction of the current customer; determine a bank self-service terminal corresponding to the current transaction of the current customer according to the transaction location; determine a biometric recognition threshold corresponding to the current transaction of the current customer according to the bank self-service terminal corresponding to the current transaction of the current customer; send the biometric recognition threshold to the second bank, wherein the second bank performs biometric recognition on the current customer according to the biometric recognition threshold; The analysis module is specifically configured to: obtain a plurality of payment locations of the transaction location of the current transaction of the current customer corresponding to the first bank; wherein the payment location is a transaction location of a transaction counterparty located near the transaction location in payment data of the first bank; obtain a customer set corresponding to the payment data of the plurality of payment locations; determine an environmental customer vector corresponding to the current transaction of the current customer according to the customer set; determine a customer vector of each self-service terminal according to transaction data of each self-service terminal of the first bank; determine the bank self-service terminal corresponding to the current transaction of the current customer according to the environmental customer vector and the customer vector; The analysis module is specifically configured to: determine a corresponding relationship between the biometric recognition threshold and the risk index according to the biometric recognition data stored in the server of the first bank, wherein for each biometric recognition threshold, when a preset matching threshold is set as the biometric recognition threshold, a proportion of biometric recognition data involving risks in the biometric recognition data stored in the server of the first bank is determined, and the proportion is taken as the risk index corresponding to the biometric recognition threshold; determine the risk index corresponding to each customer according to the biometric recognition threshold corresponding to each customer stored in the server of the first bank; for each bank self-service terminal corresponding to the current transaction of the current customer, determine the corresponding relationship between the biometric recognition threshold and the risk index of the bank self-service terminal according to the biometric recognition data of the bank self-service terminal, wherein for each biometric recognition threshold, when a preset matching threshold is set as the biometric recognition threshold, a proportion of biometric recognition data involving risks in the biometric recognition data of the bank self-service terminal is determined, and the proportion is taken as the risk index corresponding to the biometric recognition threshold. According to the risk index corresponding to the current customer and the corresponding relationship between the biometric identification threshold of each bank self-service terminal corresponding to the current transaction of the current customer and the risk index, the biometric identification threshold corresponding to the current transaction of the current customer is determined.
7. The apparatus of claim 6, wherein, The generation module is specifically configured to: determine the risk index of each customer category with respect to each transaction category; for each customer, determine a plurality of transaction categories corresponding to the customer according to the transaction data of the customer; determine a selected coefficient of each transaction category corresponding to the customer according to the customer category to which the customer belongs and the risk index of each customer category with respect to each transaction category; determine a first transaction category corresponding to the customer according to the selected coefficient; generate a customer authentication fingerprint corresponding to the customer based on the first transaction category corresponding to the customer.
8. The apparatus of claim 6, wherein, The generation module is specifically configured to: obtain transaction data of each customer category with respect to each transaction category; divide the transaction data into a plurality of transaction data subsets in chronological order, wherein the number of transactions included in each transaction data subset is greater than a first threshold, and the difference between the number of transactions included in any two transaction data subsets is less than a second threshold; take the proportion of the number of transaction data involving risks in each transaction data subset in the plurality of transaction data subsets as a risk index sample of the customer category with respect to the transaction category; determine the risk index of the customer category with respect to the transaction category as the average of the risk index samples of the customer category with respect to the transaction category.
9. The apparatus of claim 6, wherein, The generation module is specifically configured to: for each transaction category corresponding to the customer, determine a security coefficient corresponding to the transaction category according to the risk index of the customer category to which the customer belongs with respect to the transaction category; determine the selected coefficient of each transaction category corresponding to the customer according to the following calculation formula: where p i is the selected coefficient of the i j th transaction category corresponding to the customer, s j is the security coefficient corresponding to the j th transaction category corresponding to the customer.
10. The apparatus of claim 6, wherein, The generation module is specifically configured to: select a probability density function g and determine the minimum value m of the domain of the probability density function g; sort the plurality of transaction categories corresponding to the customer; for each transaction category corresponding to the customer, determine an endpoint value corresponding to the transaction category, wherein the integral value of the probability density function g from the minimum value m to the endpoint value corresponding to the transaction category is equal to the sum of the selected coefficients of all transaction categories before the transaction category in the sorting; generate a random number according to the probability density function g, and select the maximum endpoint value smaller than the random number from the endpoint values corresponding to the plurality of transaction categories corresponding to the customer, and take the transaction category corresponding to the maximum endpoint value as the first transaction category corresponding to the customer.
11. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1 to 5.
12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method of any one of claims 1 to 5.
13. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to realize the method of any one of claims 1 to 5.
Citation Information
Patent Citations
Medium-free withdrawal transaction method and device, computer equipment and readable storage medium
CN111583549A