Intelligent cloud guard system for banking business library

Abstract

The invention relates to the field of banking business library management, and particularly discloses an intelligent cloud guarding system for a banking business library, which comprises the following steps of: acquiring the time, geographic position, used equipment type and identifier, identity verification mode, identity verification failure frequency, each accessed information subitem and access duration of a bank client for logging in a bank platform; and the transaction moment, geographic position, frequency, amount, transaction type, identity verification failure times and operation duration of the bank customer on the bank platform are collected, and whether the bank customer has risks in login and transaction on the bank platform is assessed from multiple indexes, so that a risk assessment system for login and transaction of the bank customer is perfected, supervision vulnerabilities are avoided, and the risk assessment efficiency is improved. Therefore, the accuracy and reliability of account login and transaction risk assessment are improved; and meanwhile, historical login and transaction habits of the bank customer are taken as reference, so that the credibility of a result of evaluating the customer login and transaction risk is improved.

Description

Technical Field

[0001] The invention relates to the field of banking business library management, and in particular to an intelligent cloud on-duty system for banking business libraries. Background Art

[0002] Banking business database on-duty refers to the process of real-time monitoring and maintenance of the banking business database to ensure its stable operation and timely response to any abnormal situations. It plays a vital role in banking business continuity and risk management.

[0003] Among them, bank customer data security is an important aspect of bank business database duty. Existing methods identify security risks and conduct early warnings and processing by monitoring bank customers' login and transaction behaviors on bank platforms. However, the existing methods have the following shortcomings: On the one hand, the indicators of bank customers' login and transactions on bank platforms monitored by existing methods are not comprehensive enough and the coverage is not wide enough, which makes the risk assessment system incomplete and prone to regulatory loopholes, which in turn makes it difficult to determine whether there are security risks when bank customers log in and trade on bank platforms. The accuracy and reliability are low.

[0004] On the other hand, when the existing methods judge whether there are risks in the login and transaction behaviors of bank customers on the bank platform, they only use the relevant data of the set login and transaction behaviors as evaluation criteria, and then draw conclusions. They do not take into account the historical behavioral habits of bank customers and use the customers' historical login and transaction behaviors as a reference. As a result, the results of the existing methods for evaluating whether there are risks in customer logins and transactions are not credible and are prone to misjudgment. Summary of the invention

[0005] In view of the above problems, the present invention proposes an intelligent cloud duty system for a banking business library to realize the function of managing the banking business library.

[0006] The technical solution adopted by the present invention to solve its technical problem is: the present invention provides an intelligent cloud duty system for a bank business library, including: a bank customer login basic information collection module, a bank customer login risk analysis module, a bank customer transaction details information collection module, a bank customer transaction risk analysis module, a bank business library duty feedback module and a database.

[0007] The bank customer login basic information collection module is connected to the bank customer login risk analysis module, the bank customer transaction details information collection module is connected to the bank customer transaction risk analysis module, the bank business database duty feedback module is respectively connected to the bank customer login risk analysis module and the bank customer transaction risk analysis module, and the database is respectively connected to the bank customer login risk analysis module and the bank customer transaction risk analysis module.

[0008] Bank customer login basic information collection module: used to collect the basic information of bank customers currently logging into the bank platform, including login time, login geographical location, device type and identifier, identity authentication method, number of identity authentication failures, various information sub-items accessed and access duration.

[0009] Bank customer login risk analysis module: used to analyze the first risk factor, second risk factor and third risk factor of the bank customer's current login to the bank platform based on the basic information of the bank customer's current login to the bank platform, and further analyze the risk index of the bank customer's current login to the bank platform.

[0010] Bank customer transaction details information collection module: used to collect detailed information of bank customers' current transactions on the bank platform, where the detailed information includes transaction time, transaction location, transaction frequency, transaction amount, transaction type, number of identity authentication failures and transaction operation duration.

[0011] Bank customer transaction risk analysis module: used to analyze the first risk coefficient, second risk coefficient and third risk coefficient of the bank customer's current transactions on the bank platform based on the detailed information of the bank customer's current transactions on the bank platform, and further analyze the risk index of the bank customer's current transactions on the bank platform.

[0012] Bank business database duty feedback module: used to determine whether there are security risks in the current login and current transactions of bank customers based on the risk index of the bank customers' current login to the bank platform and the risk index of the bank customers' current transactions on the bank platform. If there are security risks, early warnings will be issued and feedback will be given to the bank's information security department.

[0013] Database: used to store bank customers' historical login basic information and historical transaction details on the bank platform.

[0014] Compared with the prior art, the intelligent cloud duty system for a banking business library described in the present invention has the following beneficial effects: 1. The present invention collects the time, geographic location, device type and identifier, identity authentication method, number of identity authentication failures, various information sub-items accessed and access duration when the bank customer logs into the banking platform, and evaluates whether there is a risk in the bank customer logging into the banking platform from multiple indicators, thereby improving the bank customer login risk assessment system, avoiding regulatory loopholes, and thus improving the accuracy and reliability of account login risk assessment.

[0015] 2. The present invention collects the time, geographic location, frequency, amount, transaction type, number of identity authentication failures and operation duration of bank customers' transactions on the bank platform, and evaluates whether there are risks in bank customers' transactions on the bank platform from multiple indicators, thereby improving the bank customer transaction risk assessment system, avoiding regulatory loopholes, and thus improving the accuracy and reliability of account transaction risk assessment.

[0016] 3. When judging whether there are risks in the login and transaction behaviors of bank customers on the bank platform, the present invention takes into account the historical login and transaction habits of bank customers and uses them as a reference, thereby improving the credibility of the results of assessing customer login and transaction risks and avoiding misjudgments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a system module connection diagram of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, the present invention provides an intelligent cloud duty system for a banking business library, comprising a bank customer login basic information collection module, a bank customer login risk analysis module, a bank customer transaction details information collection module, a bank customer transaction risk analysis module, a banking business library duty feedback module and a database.

[0021] The bank customer login basic information collection module is connected to the bank customer login risk analysis module, the bank customer transaction details information collection module is connected to the bank customer transaction risk analysis module, the bank business database duty feedback module is respectively connected to the bank customer login risk analysis module and the bank customer transaction risk analysis module, and the database is respectively connected to the bank customer login risk analysis module and the bank customer transaction risk analysis module.

[0022] The bank customer login basic information collection module is used to collect the basic information of the bank customer currently logging into the bank platform, wherein the basic information includes login time, login geographical location, device type and identifier, identity authentication method, number of identity authentication failures, various information sub-items accessed and access duration.

[0023] As a preferred solution, the banking platform includes but is not limited to: an Internet banking platform and a mobile banking platform. The Internet banking platform allows customers to perform account inquiries, transfers, payments and other operations through the Internet, and the mobile banking platform provides mobile applications or mobile websites so that customers can perform banking operations on mobile devices.

[0024] The bank customer login risk analysis module is used to analyze the first risk coefficient, the second risk coefficient and the third risk coefficient of the bank customer's current login to the bank platform based on the basic information of the bank customer's current login to the bank platform, and further analyze the risk index of the bank customer's current login to the bank platform.

[0025] Furthermore, the specific working process of the bank customer login risk analysis module includes: S1: extracting the basic information of the bank customer's historical login on the bank platform stored in the database, obtaining the login time of each historical login of the bank customer on the bank platform, and analyzing the login tendency ratio coefficient of each time period of a single day.

[0026] As a preferred solution, the login tendency ratio coefficient of each time period of a single day is analyzed. The specific method is: according to the login time of each historical login of the bank customer on the bank platform, the single day is divided into each time period according to the preset principle, the historical login number corresponding to each time period of a single day is counted, and the historical login number corresponding to each time period of a single day is divided by the total number of historical logins to obtain the login tendency ratio coefficient of each time period of a single day.

[0027] In a specific embodiment, a single day is divided into time periods according to a preset principle of equal duration.

[0028] According to the current login time of the bank customer to the bank platform, the login tendency ratio coefficient of the time period in which the current login time of the bank customer to the bank platform falls is obtained by screening, and is recorded as α.

[0029] S2: Obtain a set of geographical locations commonly used by bank customers to log in.

[0030] As a preferred solution, the basic information of historical logins of bank customers on the bank platform stored in the database is extracted to obtain the geographical locations of the bank customers' historical logins on the bank platform, and the geographical locations commonly used by bank customers for login are summarized.

[0031] As a preferred solution, the geographical location may be an IP address or GPS coordinates, etc.

[0032] The current login geographical location of the bank customer logging into the bank platform is compared with the set of geographical locations commonly used by the bank customer to log in, and it is determined whether the current login geographical location of the bank customer logging into the bank platform is a common login geographical location or an uncommon login geographical location.

[0033] As a preferred solution, if the current geographical location of the bank customer logging into the bank platform does not belong to the set of geographical locations commonly used by bank customers for logging in, the current geographical location of the bank customer logging into the bank platform will be recorded as an uncommon geographical location; otherwise, the current geographical location of the bank customer logging into the bank platform will be recorded as a common geographical location.

[0034] Set the risk factors corresponding to the uncommon login geographical locations and the common login geographical locations, filter the risk factors corresponding to the login geographical locations where bank customers currently log into the bank platform, and record them as β.

[0035] S3: Obtain the device types and identifiers commonly used by bank customers to log in.

[0036] As a preferred solution, the basic information of the bank customers' historical logins on the bank platform stored in the database is extracted to obtain the types and identifiers of the devices used by the bank customers for each historical login on the bank platform, and the types of devices and their identifiers commonly used by bank customers for login are summarized.

[0037] As a preferred solution, the types of devices used include but are not limited to: mobile phones, computers, tablets, etc.

[0038] As a preferred solution, the device identifier used includes but is not limited to: MAC address, IMEI number, etc.

[0039] The device type and identifier currently used by the bank customer to log in to the bank platform are compared with the device types and identifiers commonly used by the bank customer to log in, and the risk factor corresponding to the device currently used by the bank customer to log in to the bank platform is analyzed and recorded as γ.

[0040] As a preferred solution, the risk factors corresponding to the device that the bank customer is currently using to log into the bank platform are analyzed. The specific method is: compare the type and identifier of the device that the bank customer is currently using to log into the bank platform with the types of devices and their identifiers that the bank customer commonly uses to log in. If the type and identifier of the device that the bank customer is currently using to log into the bank platform are consistent with a certain device type and its identifier that the bank customer commonly uses to log in, then the device that the bank customer is currently using to log into the bank platform is recorded as a commonly used login device; if the type and identifier of the device that the bank customer is currently using to log into the bank platform are inconsistent with all the device types and their identifiers that the bank customer commonly uses to log in, then the device that the bank customer is currently using to log into the bank platform is recorded as an uncommon login device.

[0041] Set risk factors corresponding to uncommon login devices and common login devices, and screen risk factors corresponding to devices currently used by bank customers to log into the bank platform.

[0042] S4: By analyzing the formula The first risk coefficient φ1 of the bank customer currently logging into the bank platform is obtained, where b1, b2, and b3 represent the weights of the preset login time, login geographical location, and login device, respectively, and b1+b2+b3=1.

[0043] Furthermore, the specific working process of the bank customer login risk analysis module also includes: obtaining a set of identity authentication methods commonly used by bank customers for login.

[0044] As a preferred solution, the basic information of the bank customers' historical logins on the bank platform stored in the database is extracted to obtain the identity authentication methods of the bank customers' historical logins on the bank platform, and a set of identity authentication methods commonly used by bank customers for login is summarized.

[0045] As a preferred solution, identity authentication methods include but are not limited to: passwords, one-time verification codes such as SMS verification codes and email verification codes, fingerprint recognition, face recognition, iris scanning and other biometric recognition, etc.

[0046] The identity verification method currently used by bank customers to log into the bank platform is compared with the set of identity verification methods commonly used by bank customers to log in, and the risk factor corresponding to the identity verification method currently used by bank customers to log into the bank platform is analyzed and recorded as ε1.

[0047] As a preferred solution, the risk factors corresponding to the identity verification method currently used by bank customers to log into the bank platform are analyzed. The specific method is: the identity verification method currently used by the bank customer to log into the bank platform is compared with the set of identity verification methods commonly used by bank customers to log in. If the identity verification method currently used by the bank customer to log into the bank platform does not belong to the set of identity verification methods commonly used by bank customers to log in, then the identity verification method currently used by the bank customer to log into the bank platform is recorded as an uncommon identity verification method. Otherwise, the identity verification method currently used by the bank customer to log into the bank platform is recorded as a common identity verification method.

[0048] Set the risk factors corresponding to uncommon identity authentication methods and common identity authentication methods, and filter the risk factors corresponding to the identity authentication methods currently used by bank customers to log into the bank platform.

[0049] Set the security levels of various identity authentication methods and the risk factors corresponding to each security level, and select the risk factor corresponding to the security level of the identity authentication method currently used by bank customers to log into the bank platform, which is recorded as ε2.

[0050] The number of identity authentication failures of the bank customer currently logging into the bank platform is recorded as c.

[0051] By analyzing the formula φ2=(ε1+ε2)*(1+c*δ Δc ) to obtain the second risk coefficient φ2 of the bank customer currently logging into the bank platform, where δ Δc Indicates the impact factor corresponding to the preset number of unit login identity authentication failures.

[0052] Furthermore, the specific working process of the bank customer login risk analysis module also includes: D1: obtaining a set of information sub-items frequently accessed by bank customers when logging in.

[0053] As a preferred solution, the basic information of bank customers' historical logins on the bank platform stored in the database is extracted to obtain the information sub-items visited by the bank customers at each historical login on the bank platform, and the information sub-items frequently visited by the bank customers are summarized to obtain a set of information sub-items.

[0054] Compare each information sub-item currently accessed by the bank customer when logging into the bank platform with the set of information sub-items frequently accessed by the bank customer when logging into the bank platform, and obtain the number of non-commonly accessed information sub-items currently accessed by the bank customer when logging into the bank platform, which is recorded as d1.

[0055] As a preferred solution, the number of information sub-items that the bank customer currently accesses when logging into the bank platform and that are not in the set of information sub-items that the bank customer frequently accesses when logging into the bank platform is recorded as the number of information sub-items that the bank customer currently accesses when logging into the bank platform.

[0056] D2: Set a set of sensitive information sub-items in the banking platform, compare each information sub-item currently accessed by the bank customer logging into the banking platform with the set of sensitive information sub-items, and screen the number of sensitive information sub-items currently accessed by the bank customer logging into the banking platform, which is recorded as d2.

[0057] D3: The duration of the bank customer's current login to the bank platform is recorded as t.

[0058] Get the average access time of bank customers' historical single logins and record it as t 均 .

[0059] As a preferred solution, the basic information of historical logins of bank customers on the bank platform stored in the database is extracted to obtain the access duration of each historical login of the bank customer on the bank platform, and further obtain the average access duration of each historical single login of the bank customer.

[0060] D4: By analyzing the formula Get the third risk factor φ3 of the bank customer currently logging into the bank platform, where They respectively represent the influencing factors of the preset number of unit non-habitually accessed information sub-items and the number of unit sensitive information sub-items.

[0061] Furthermore, the specific working process of the bank customer login risk analysis module also includes: calculating the weighted average of the first risk coefficient, the second risk coefficient and the third risk coefficient of the bank customer currently logging into the bank platform to obtain the risk index of the bank customer currently logging into the bank platform.

[0062] As a preferred solution, the weights of the first risk factor, the second risk factor and the third risk factor of the bank customer currently logging into the bank platform are set values, and the cumulative sum is 1.

[0063] It should be noted that the present invention collects the time, geographic location, device type and identifier, identity authentication method, number of identity authentication failures, various information sub-items accessed and access duration when the bank customer logs into the bank platform, and evaluates whether there is a risk in the bank customer logging into the bank platform from multiple indicators, thereby improving the bank customer login risk assessment system, avoiding regulatory loopholes, and thus improving the accuracy and reliability of account login risk assessment.

[0064] The bank customer transaction details information collection module is used to collect details of bank customers' current transactions on the bank platform, where the details include transaction time, transaction location, transaction frequency, transaction amount, transaction type, number of identity authentication failures and transaction operation duration.

[0065] As a preferred solution, the transaction frequency of the bank customer's current transactions on the bank platform is obtained. The specific method is: obtain the interval between the bank customer's current transaction on the bank platform and the last transaction, and perform an inverse calculation to obtain the transaction frequency of the bank customer's current transactions on the bank platform.

[0066] The bank customer transaction risk analysis module is used to analyze the first risk coefficient, the second risk coefficient and the third risk coefficient of the bank customer's current transactions on the bank platform according to the detailed information of the bank customer's current transactions on the bank platform, and further analyze the risk index of the bank customer's current transactions on the bank platform.

[0067] Furthermore, the specific working process of the bank customer transaction risk analysis module includes: F1: extracting the historical transaction details of the bank customer on the bank platform stored in the database, obtaining the transaction time of each historical transaction of the bank customer on the bank platform, dividing the single day into time periods according to preset principles, and further obtaining the historical transaction times corresponding to each time period of the single day.

[0068] According to the transaction time of the bank customer's current transaction on the bank platform, the number of historical transactions corresponding to the time period of the transaction time of the bank customer's current transaction on the bank platform is screened and recorded as q.

[0069] F2: Get the set of geographical locations commonly used by bank customers for transactions.

[0070] As a preferred solution, the historical transaction details of bank customers on the bank platform stored in the database are extracted to obtain the transaction geographical locations of each historical transaction of the bank customer on the bank platform, and the geographical locations commonly used by bank customers for transactions are summarized.

[0071] The geographical location of bank customers' current transactions on the bank platform is compared with the geographical location set commonly used by bank customers for transactions, and the risk factor corresponding to the geographical location of bank customers' current transactions on the bank platform is analyzed, which is recorded as η.

[0072] As a preferred solution, the risk factors corresponding to the geographical location of the bank customer's current transactions on the bank platform are analyzed. The specific method is: compare the geographical location of the bank customer's current transactions on the bank platform with the set of geographical locations commonly used by the bank customer for transactions. If the geographical location of the bank customer's current transactions on the bank platform does not belong to the set of geographical locations commonly used for its transactions, then the geographical location of the bank customer's current transactions on the bank platform is recorded as an uncommon geographical location; otherwise, the geographical location of the bank customer's current transactions on the bank platform is recorded as a commonly used geographical location.

[0073] Set risk factors corresponding to uncommon transaction geographic locations and common transaction geographic locations, and filter risk factors corresponding to transaction geographic locations of bank customers who are currently trading on the bank platform.

[0074] F3: By analyzing the formula The first risk coefficient κ1 of the bank customer's current transaction on the bank platform is obtained, where q0 represents the threshold of the number of historical transactions corresponding to the time period of the transaction moment.

[0075] Furthermore, the specific working process of the bank customer transaction risk analysis module also includes: G1: obtaining the average transaction frequency of the bank customer's historical single transactions, which is recorded as f 均 .

[0076] As a preferred solution, the historical transaction details of bank customers on the bank platform stored in the database are extracted to obtain the transaction frequency of each historical transaction of the bank customer on the bank platform, and the average value is calculated to obtain the average transaction frequency of the bank customer's historical single transaction.

[0077] G2: Get the average transaction amount of a bank customer's historical single transaction and record it as p 均 .

[0078] As a preferred solution, the historical transaction details of bank customers on the bank platform stored in the database are extracted to obtain the transaction amounts of each historical transaction of the bank customer on the bank platform, and the average value is calculated to obtain the average transaction amount of the bank customer's historical single transaction.

[0079] As a preferred option, large transactions may carry higher risks than small transactions.

[0080] G3: Get a set of transaction types commonly used by bank customers.

[0081] As a preferred solution, the historical transaction details of bank customers on the bank platform stored in the database are extracted to obtain the transaction types of each historical transaction of the bank customer on the bank platform, and the transaction types commonly used by the bank customer are summarized to obtain a set of transaction types.

[0082] As a preferred solution, transaction types include but are not limited to: deposit, withdrawal, transfer, payment, etc.

[0083] Compare the transaction types that bank customers currently trade on the bank platform with the set of transaction types commonly used by bank customers, analyze the risk factors corresponding to the transaction types that bank customers currently trade on the bank platform, and record them as λ1.

[0084] Set the risk factors corresponding to various transaction types, filter the risk factors corresponding to the transaction types currently traded by bank customers on the bank platform, and record them as λ2.

[0085] As a preferred solution, the risk factors corresponding to the transaction types currently traded by bank customers on the bank platform are analyzed. The specific method is: compare the transaction types currently traded by bank customers on the bank platform with the set of transaction types commonly used by bank customers. If the transaction type currently traded by the bank customer on the bank platform does not belong to the set of transaction types commonly used by its transactions, then the transaction type currently traded by the bank customer on the bank platform is recorded as an uncommon transaction type. Otherwise, the transaction type currently traded by the bank customer on the bank platform is recorded as a common transaction type.

[0086] Set risk factors corresponding to uncommon transaction types and common transaction types, and filter risk factors corresponding to transaction types currently traded by bank customers on the bank platform.

[0087] G4: By analyzing the formula The second risk coefficient κ2 of the bank customer's current transactions on the bank platform is obtained, where e represents a natural constant, and f and p represent the transaction frequency and transaction amount of the bank customer's current transactions on the bank platform, respectively.

[0088] Furthermore, the specific working process of the bank customer transaction risk analysis module also includes: H1: the number of identity authentication failures and transaction operation duration of the bank customer's current transactions on the bank platform are recorded as n and t' respectively.

[0089] H2: Obtain the reference operation time of bank customers’ current transactions on the bank platform, and record it as t′ 参 .

[0090] As a preferred solution, the historical transaction details of bank customers on the bank platform stored in the database are extracted to obtain the average operation time of various types of transactions performed by bank customers on the bank platform, which is recorded as the reference operation time for various types of transactions. According to the transaction type currently performed by the bank customer on the bank platform, the reference operation time of the bank customer's current transactions on the bank platform is screened.

[0091] H3: Obtain the credit score level of bank customers from credit rating agencies, set the credit score impact factor corresponding to each credit score level, screen and obtain the credit score impact factor of bank customers, and record it as μ.

[0092] As a preference, bank customers with lower credit scores may be higher risk.

[0093] H4: By analyzing the formula Get the third risk coefficient κ3 of the bank customer's current transaction on the bank platform, where σ Δnrepresents the impact factor corresponding to the preset number of failed identity authentications per unit transaction, and Δκ3 represents the compensation amount of the third risk coefficient of the preset bank customer's current transactions on the bank platform.

[0094] Furthermore, the specific working process of the bank customer transaction risk analysis module also includes: calculating the weighted average of the first risk coefficient, the second risk coefficient and the third risk coefficient of the bank customer's current transactions on the bank platform to obtain the risk index of the bank customer's current transactions on the bank platform.

[0095] As a preferred solution, the weights of the first risk coefficient, the second risk coefficient and the third risk coefficient of the bank customer's current transactions on the bank platform are set values, and the cumulative sum is 1.

[0096] It should be noted that the present invention collects the time, geographic location, frequency, amount, transaction type, number of identity authentication failures and operation duration of bank customers' transactions on the bank platform, and evaluates whether there are risks in bank customers' transactions on the bank platform from multiple indicators, thereby improving the bank customer transaction risk assessment system, avoiding regulatory loopholes, and thus improving the accuracy and reliability of account transaction risk assessment.

[0097] The bank business database on-duty feedback module is used to determine whether there is a security risk in the bank customer's current login and current transaction based on the risk index of the bank customer's current login to the bank platform and the risk index of the bank customer's current transactions on the bank platform. If there is a security risk, an early warning is issued and feedback is given to the bank's information security department.

[0098] Furthermore, the specific working process of the bank business database on-duty feedback module is: comparing the risk index of the bank customer currently logging into the bank platform with the preset risk index threshold. If the risk index of the bank customer currently logging into the bank platform is greater than the preset risk index threshold, there is a security risk in the bank customer's current login, and an early warning is issued. Similarly, it is determined whether there is a security risk in the bank customer's current transaction, and further feedback is provided to the bank's information security department.

[0099] As a preferred solution, if it is identified that there is a risk in the bank customer's login, the bank platform can send a text message to notify the bank customer.

[0100] As a preferred solution, if a bank customer's transaction is identified as risky, the bank platform can send a text message to notify the bank customer and add additional verification steps, such as two-factor authentication and manual review.

[0101] It should be noted that when judging whether there are risks in the login and transaction behaviors of bank customers on the bank platform, the present invention takes into account the bank customer's historical login and transaction habits and uses them as a reference, thereby improving the credibility of the results of assessing customer login and transaction risks and avoiding misjudgments.

[0102] The database is used to store the basic historical login information and historical transaction details of bank customers on the bank platform.

[0103] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent cloud-based on-call system for a banking service database, characterized in that: include: Bank customer login basic information collection module: used to collect the basic information of bank customers currently logging into the banking platform, including login time, login location, device type and identifier, identity authentication method, number of identity authentication failures, various information sub-items accessed, and access duration; Bank customer login risk analysis module: used to analyze the first risk factor, the second risk factor and the third risk factor of the bank customer's current login to the bank platform according to the basic information of the bank customer's current login to the bank platform, and further analyze the risk index of the bank customer's current login to the bank platform; Bank customer transaction details information collection module: used to collect detailed information of bank customers' current transactions on the bank platform, where the detailed information includes transaction time, transaction location, transaction frequency, transaction amount, transaction type, number of identity authentication failures and transaction operation duration; Bank customer transaction risk analysis module: used to analyze the first risk coefficient, the second risk coefficient and the third risk coefficient of the bank customer's current transactions on the bank platform according to the detailed information of the bank customer's current transactions on the bank platform, and further analyze the risk index of the bank customer's current transactions on the bank platform; Bank business database duty feedback module: used to judge whether there are security risks in the current login and current transaction of the bank customer according to the risk index of the bank customer's current login to the bank platform and the risk index of the bank customer's current transaction on the bank platform. If there is a security risk, an early warning is issued and feedback is given to the bank's information security department; Database: used to store bank customers' historical login basic information and historical transaction details on the bank platform.

2. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the bank customer login risk analysis module includes: S1: extract the basic information of historical logins of bank customers on the bank platform stored in the database, obtain the login time of each historical login of the bank customer on the bank platform, and analyze the login tendency ratio coefficient of each time period of a single day; According to the current login time of the bank customer to the bank platform, the login tendency ratio coefficient of the time period in which the current login time of the bank customer to the bank platform falls is obtained by screening, and is recorded as α; S2: Obtain a set of geographical locations commonly used by bank customers to log in; Compare the current login geographical location of the bank customer logging into the bank platform with the set of geographical locations commonly used by the bank customer to log in, and determine whether the current login geographical location of the bank customer logging into the bank platform is a common login geographical location or an uncommon login geographical location; Set the risk factors corresponding to the uncommon login geographical locations and the common login geographical locations, and select the risk factor corresponding to the login geographical location of the bank customer currently logging into the bank platform, which is recorded as β; S3: Obtain various device types and their identifiers commonly used by bank customers to log in; Compare the device type and identifier currently used by the bank customer to log in to the bank platform with the device types and identifiers commonly used by the bank customer to log in, analyze the risk factor corresponding to the device currently used by the bank customer to log in to the bank platform, and record it as γ; S4: By analyzing the formula The first risk coefficient φ1 of the bank customer currently logging into the bank platform is obtained, where b1, b2, and b3 represent the weights of the preset login time, login geographical location, and login device, respectively, and b1+b2+b3=1.

3. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the bank customer login risk analysis module also includes: Get a collection of common authentication methods used by bank customers to log in; Compare the identity verification method currently used by bank customers to log into the bank platform with the set of identity verification methods commonly used by bank customers to log in, analyze the risk factor corresponding to the identity verification method currently used by bank customers to log into the bank platform, and record it as ε1; Set the security level of various identity authentication methods and the risk factors corresponding to each security level, and select the risk factor corresponding to the security level of the identity authentication method currently used by the bank customer to log in to the bank platform, which is recorded as ε2; The number of identity authentication failures of the bank customer currently logging into the bank platform is recorded as c; By analyzing the formula φ2=(ε1+ε2)*(1+c*δ Δc ) to obtain the second risk coefficient φ2 of the bank customer currently logging into the bank platform, where δ Δc Indicates the impact factor corresponding to the preset number of unit login identity authentication failures.

4. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the bank customer login risk analysis module also includes: D1: Get the information sub-item set that bank customers often access when logging in; Compare each information sub-item currently accessed by the bank customer when logging into the bank platform with the set of information sub-items that the bank customer frequently accesses when logging into the bank platform, and obtain the number of non-commonly accessed information sub-items that the bank customer currently accesses when logging into the bank platform, which is recorded as d1; D2: Set a set of sensitive information sub-items in the banking platform, compare each information sub-item currently accessed by the bank customer logging into the banking platform with the set of sensitive information sub-items, and select the number of sensitive information sub-items currently accessed by the bank customer logging into the banking platform, which is recorded as d2; D3: The current access time of the bank customer logging into the bank platform is recorded as t; Get the average access time of bank customers' historical single logins and record it as t 均 ; D4: By analyzing the formula Get the third risk factor φ3 of the bank customer currently logging into the bank platform, where They respectively represent the influencing factors of the preset number of unit non-habitually accessed information sub-items and the number of unit sensitive information sub-items.

5. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the bank customer login risk analysis module also includes: A weighted average value is calculated for the first risk coefficient, the second risk coefficient and the third risk coefficient of the bank customer currently logging into the bank platform to obtain a risk index of the bank customer currently logging into the bank platform.

6. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the bank customer transaction risk analysis module includes: F1: extract the historical transaction details of the bank customer on the bank platform stored in the database, obtain the transaction time of each historical transaction of the bank customer on the bank platform, divide the single day into various time periods according to the preset principle, and further calculate the historical transaction times corresponding to each time period of the single day; According to the transaction time of the bank customer's current transaction on the bank platform, the historical transaction times corresponding to the time period of the transaction time of the bank customer's current transaction on the bank platform are screened and recorded as q; F2: Get the set of geographical locations commonly used by bank customers for transactions; Compare the current transaction location of the bank customer on the bank platform with the set of common transaction locations of the bank customer, analyze the risk factor corresponding to the current transaction location of the bank customer on the bank platform, and record it as η; F3: By analyzing the formula The first risk coefficient κ1 of the bank customer's current transaction on the bank platform is obtained, where q0 represents the threshold of the number of historical transactions corresponding to the time period of the transaction moment.

7. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the bank customer transaction risk analysis module also includes: G1: Get the average transaction frequency of a bank customer's historical single transaction, and record it as f 均 ; G2: Get the average transaction amount of a bank customer's historical single transaction and record it as p 均 ; G3: Get the transaction type set commonly used by bank customers; Compare the transaction types currently traded by bank customers on the bank platform with the set of transaction types commonly used by bank customers, analyze the risk factors corresponding to the transaction types currently traded by bank customers on the bank platform, and record them as λ1; Set the risk factors corresponding to various transaction types, select the risk factors corresponding to the transaction types currently traded by bank customers on the bank platform, and record them as λ2; G4: By analyzing the formula The second risk coefficient κ2 of the bank customer's current transactions on the bank platform is obtained, where e represents a natural constant, and f and p represent the transaction frequency and transaction amount of the bank customer's current transactions on the bank platform, respectively.

8. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the bank customer transaction risk analysis module also includes: H1: The number of identity authentication failures and transaction operation duration of bank customers in the current banking platform transactions are recorded as n and t′ respectively; H2: Obtain the reference operation time of bank customers’ current transactions on the bank platform, and record it as t′ 参 ; H3: Obtain the credit score level of bank customers from the credit rating agency, set the credit score impact factor corresponding to each credit score level, screen and obtain the credit score impact factor of bank customers, and record it as μ; H4: By analyzing the formula Get the third risk coefficient κ3 of the bank customer's current transaction on the bank platform, where σ Δn represents the impact factor corresponding to the preset number of failed identity authentications per unit transaction, and Δκ3 represents the compensation amount of the third risk coefficient of the preset bank customer's current transactions on the bank platform.

9. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the bank customer transaction risk analysis module also includes: A weighted average value is calculated for the first risk coefficient, the second risk coefficient and the third risk coefficient of the bank customer's current transactions on the bank platform to obtain a risk index for the bank customer's current transactions on the bank platform.

10. The intelligent cloud-based on-call system for a banking service database according to claim 1, characterized in that: The specific working process of the banking business database on-duty feedback module is as follows: The risk index of the bank customer's current login to the bank platform is compared with the preset risk index threshold. If the risk index of the bank customer's current login to the bank platform is greater than the preset risk index threshold, there is a security risk in the bank customer's current login, and an early warning is issued. Similarly, it is determined whether there is a security risk in the bank customer's current transaction, and further feedback is given to the bank's information security department.

Images