A method, device, electronic device and storage medium for credit deposit

Abstract

This application discloses a deposit credit authorization method, device, electronic device, and storage medium. The method includes: obtaining the net assets, deposit and withdrawal records, and transaction records of the customer initiating the deposit; determining the customer type based on the customer's net assets, deposit and withdrawal records, and transaction records; and determining the credit funds to be granted to the customer based on a pre-built credit authorization model corresponding to the customer type. Through this method, customers can immediately use more funds for transactions after initiating a deposit without incurring additional fees, and brokerages can effectively reduce financial losses caused by customers' ACH reversals.

Description

Technical Field

[0001] The present invention generally relates to the field of financial technology, and in particular to a method, device, electronic device and storage medium for granting credit for deposits. Background Art

[0002] When a client deposits funds into their brokerage account at a US brokerage, the actual flow of funds is a transfer of the cash on the client's bank card to the brokerage's bank account (more accurately, the brokerage's settlement account held in escrow at the bank). Currently, inter-bank transfers in the US use the ACH (Auto Clearing House) system. The bank account that disburses funds is called the Originator, and the bank account that receives the funds is called the Receiver.

[0003] The ACH system is available to almost all US banks and is inexpensive, making it the mainstream method for daily bank transfers. However, the ACH system does not settle in real time, but rather in batches at the end of each day. Furthermore, the settlement period varies between banks. Therefore, during the actual end-of-day settlement, the initiator's bank account may sometimes have insufficient balance or other reasons that prevent the initiator's previous deposit from being successfully transferred to the recipient. This is known as an ACH Reversal.

[0004] The existence of ACH Reversals has a significant impact on brokerage firms' operations. For example, if client A initiates a deposit on day T, since client A's cash is not settled in real time on day T, this deposit may be reversed when the actual settlement is completed in the future, meaning that the deposit will not be credited to the account. This leaves the brokerage firm with a difficult question: if client A initiates a deposit of M on day T, should client A be allowed to use M funds?

[0005] Currently, several approaches are being used to address the issue of credit access for customer deposits: ① Denying credit, meaning brokers will not allow clients to use their funds until clearing is complete. This approach is the safest, but it significantly impacts the efficiency of client fund utilization and, consequently, hinders brokerages' customer acquisition. ② Guiding clients to use affiliated banks to complete deposits. US commercial banks are permitted to engage in mixed operations, leading some US brokerages to also operate commercial banking businesses within their group. These brokerages offer policies such as expedited account transfers to encourage clients to open bank cards within their group and initiate deposits using these cards. Since they operate within the same group, real-time clearing is possible, eliminating reversals. However, this approach is only suitable for large, integrated commercial institutions and has a high barrier to entry, making it unsuitable for new online brokerages. ③ Using a membership fee model to cover risk losses. New US online brokerages offer credit access as a service. Only by becoming a member of the brokerage and paying a monthly membership fee can clients access the credit available for trading after depositing funds. This approach shifts the brokerage's risk losses onto clients, increasing their expenses. Summary of the Invention

[0006] In view of the above-mentioned defects or deficiencies in the prior art, it is desired to provide a deposit credit method, device, electronic device and storage medium.

[0007] In a first aspect, the present invention provides a deposit credit granting method, the method comprising:

[0008] Obtain the net assets, deposit and withdrawal records, and transaction records of the client who initiated the deposit;

[0009] Determine the client's customer type based on the client's net assets, deposit and withdrawal records, and transaction records;

[0010] Determine the credit funds to be granted to customers based on the pre-built credit model corresponding to the customer type.

[0011] In one embodiment, determining the customer type of a customer based on the customer's net assets, deposit and withdrawal records, and transaction records includes:

[0012] If the customer's net assets are 0 and the customer has no deposit or withdrawal records and no transaction records, the customer is determined to be the first customer type;

[0013] Otherwise, the customer is determined to be the second customer type.

[0014] In one embodiment, determining the credit funds to be granted to a customer based on a pre-built credit model corresponding to the customer type includes:

[0015] If the customer type is the first customer type, determining the credit funds to be granted to the customer based on a pre-established first credit model, wherein the first credit model is a credit model corresponding to the first customer type;

[0016] If the customer type is the second customer type, the credit funds to be granted to the customer are determined based on a pre-built second credit model, wherein the second credit model is a credit model corresponding to the second customer type.

[0017] In one embodiment, the first credit model is constructed by the following steps:

[0018] Obtaining strongly associated attribute information and weakly associated attribute information of customers of the first customer type;

[0019] All customers of the first customer type who have the same strongly correlated attribute information are grouped as the first customer group;

[0020] The second customer group includes all customers of the first customer type who have the same weakly correlated attribute information as the customers in the first customer group.

[0021] The first customer group or the second customer group is used as the first credit model.

[0022] In one embodiment, determining the credit funds to be granted to the customer based on the pre-built first credit model includes:

[0023] If any customer in the first customer group is confirmed to be a bad customer, no credit will be granted to all customers in the first customer group;

[0024] If any customer in the second customer group is confirmed to be a bad customer, whether to grant credit to the remaining customers in the second customer group is determined based on the staining rate;

[0025] If the staining rate of the second customer group is greater than a preset threshold, no credit will be granted to all customers in the second customer group.

[0026] In one embodiment, the method further comprises:

[0027] Get the number of dyed customers and the number of undyed customers in the second customer group;

[0028] Obtain the weights of undyed customers associated with dyed customers through weakly associated attribute information;

[0029] Determine the dyeing rate based on the number of dyed customers, the number of undyed customers, and the weight.

[0030] In one embodiment, the second trust model is constructed by the following steps:

[0031] Obtain the effective net assets, risk tolerance and repayment willingness of the second type of customers;

[0032] Determine the customer's cash credit limit based on the customer's effective net assets, risk tolerance, and willingness to repay;

[0033] Construct a second credit model based on the customer's credit cash limit, global credit limit and customer's funds in transit.

[0034] In one embodiment, the method further comprises:

[0035] Obtain the client's actual net worth;

[0036] Determine the client's effective net assets based on the client's actual net assets.

[0037] In one embodiment, the method further comprises:

[0038] Obtain the maximum asset mortgage ratio, global leverage ratio, total market value of long stock positions, total market value of short stock positions, and client remaining cash;

[0039] The leverage ratio at the end of the day is determined based on the total market value of long stock positions, the total market value of short stock positions, and the client's remaining cash;

[0040] Determine the average leverage ratio for customers based on the end-of-day leverage ratio and the number of trading days;

[0041] Determine the customer's risk tolerance based on the customer's average leverage ratio, maximum asset mortgage ratio and global leverage coefficient.

[0042] In one embodiment, the method further comprises:

[0043] Obtain the number of normal and received deposits, the number of recalled deposits within the due date, the number of recalled deposits overdue, and the total deposit amount received by the customer;

[0044] Determine the actual deposit efficiency based on the total deposit amount received, the number of recalls for non-overdue deposits, and the number of recalls for overdue deposits;

[0045] Determine the customer's willingness to repay based on the actual deposit efficiency, the number of normal and received deposits, and the number of overdue deposit recalls.

[0046] In a second aspect, the present invention provides a deposit credit authorization device, the device comprising:

[0047] The acquisition module is used to obtain the net assets, deposit and withdrawal records, and transaction records of the customer who initiated the deposit;

[0048] The first determination module is used to determine the customer type of the customer based on the customer's net assets, deposit and withdrawal records, and transaction records;

[0049] The second determining module is used to determine the credit funds to be given to the customer based on a pre-built credit model corresponding to the customer type.

[0050] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the deposit credit method of the first aspect is implemented.

[0051] In a fourth aspect, the present invention provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the deposit credit authorization method of the first aspect.

[0052] The present application discloses a deposit credit granting method, device, electronic device, and storage medium. This method determines the credit funds granted to different customer types based on the credit granting models corresponding to each customer type. This method can maximize the reasonable credit cash granted to customers while ensuring limited risk. Customers can immediately use more funds for transactions after initiating a deposit without incurring additional fees. For brokerage firms, this method can effectively reduce financial losses caused by customer ACH reversals while improving the customer experience at the brokerage firm and increasing brokerage customer acquisition and retention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0054] Figure 1 A flowchart of a deposit credit authorization method provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of a first customer group provided by an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of a second customer group provided by an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of coloring bad customers in the first customer group provided by an embodiment of the present invention;

[0058] Figure 5 A schematic diagram of the structure of a deposit credit authorization device provided in an embodiment of the present invention;

[0059] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0061] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 those skilled in the art without creative work are within the scope of protection of this application.

[0062] The terms "first," "second," "third," "fourth," and so on (if any) in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described can be implemented in orders other than those illustrated or described herein.

[0063] In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such process, method, product or apparatus.

[0064] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0065] For US brokerages, the existence of ACH Reversal can have a significant impact on their operations. From a risk perspective, it's safest for brokerages to not allow clients to access these funds before clearing is complete. However, this can significantly impact the efficiency of client funds, significantly impacting the brokerage's customer acquisition. Allowing clients to access these funds could lead to losses if the deposit is ultimately recalled. Therefore, brokerages need to strike a balance between losses and gains, managing risk while providing clients with the most reasonable deposits.

[0066] To solve the above problem, it is essentially necessary to determine the amount of funds N that the brokerage can grant to the customer when the customer initiates a deposit M. N is called credit cash, which means that the brokerage will grant the customer credit funds based on the customer's credit situation.

[0067] The practice of calculating customer credit is often used in bank card business. Banks grant different credit limits to different customers, generally based on the following customer information:

[0068] a) Property information: asset information, income information, consumption information, etc.;

[0069] b) Customer personal information: age, region, occupation, etc.;

[0070] c) The customer's repayment and default history;

[0071] d) Customer's associated accounts: such as the same IP, device, same bank card (joint bank card), and

[0072] e) Third-party credit reporting system score.

[0073] The bank will score the customer based on this basic information or directly calculate the customer's risk level weight, thereby giving the customer a credit limit, and then adjust the credit limit based on the updated data of the customer's above information.

[0074] Therefore, this application proposes a deposit credit method that can maximize reasonable credit cash to customers while ensuring limited risks.

[0075] Reference Figure 1 , which shows a flow chart of a deposit credit authorization method described according to an embodiment of the present application.

[0076] like Figure 1 As shown, a deposit credit method may include:

[0077] S110. Obtain the net assets, deposit and withdrawal records, and transaction records of the customer who initiated the deposit;

[0078] S120. Determine the customer type based on the customer's net assets, deposit and withdrawal records, and transaction records;

[0079] S130: Determine the credit funds to be granted to the customer based on the pre-built credit model corresponding to the customer type.

[0080] Specifically, if we compare the credit approval process for customer deposits to a bank setting different credit card limits for different customers, then we need to understand the customer's basic information, current assets, and historical behavior. We also need to understand the customer's historical reversal situations, that is, the overdue rate.

[0081] At the same time, for different customer groups, the weights of various information need to be differentiated. For example, for new customers, since their historical transaction and deposit and withdrawal records are blank, the weight of the customer's social relationship and basic information needs to be increased. For old customers, that is, customers who have conducted transactions and deposits and withdrawals, the weight of their transaction behavior and mortgageable assets needs to be increased.

[0082] In summary, this application handles new and existing customers differently. It should be noted that new and existing customers here refer to different customer types. Existing and new customers are categorized based on their net assets, deposit and withdrawal records, and transaction history.

[0083] The embodiments of the present application determine the credit funds granted to customers based on the credit models corresponding to different customer types, thereby maximizing the reasonable credit cash granted to customers while ensuring limited risk. For customers, after initiating a deposit, they can immediately use more funds for transactions without incurring additional fees. For brokerage firms, this improves the customer experience at the brokerage firm, improves customer acquisition and retention, and effectively reduces financial losses caused by customer ACH reversals.

[0084] Optionally, if the customer's net assets are 0 and the customer has no deposit or withdrawal records and no transaction records, the customer is determined to be the first customer type, that is, a new customer. Customers with no deposit or withdrawal records include both successful and failed deposits and withdrawals.

[0085] Otherwise, the customer is determined to be the second customer type, that is, an old customer.

[0086] Different credit models are constructed for different customer types. The first customer type corresponds to the first credit model, and the second customer type corresponds to the second credit model.

[0087] In one embodiment, determining the credit funds to be granted to a customer based on a pre-built credit model corresponding to the customer type includes:

[0088] If the customer type is the first customer type, determining the credit funds to be granted to the customer based on a pre-established first credit model, wherein the first credit model is a credit model corresponding to the first customer type;

[0089] If the customer type is the second customer type, the credit funds to be granted to the customer are determined based on a pre-built second credit model, wherein the second credit model is a credit model corresponding to the second customer type.

[0090] In one embodiment, the first credit model is constructed by the following steps:

[0091] Obtaining strongly associated attribute information and weakly associated attribute information of customers of the first customer type;

[0092] All customers of the first customer type who have the same strongly correlated attribute information are grouped as the first customer group;

[0093] Any customers with the same weakly associated attribute among the remaining customers of the first customer type except the customers in the first customer group are used as the second customer group;

[0094] The first customer group or the second customer group is used as the first credit model.

[0095] Specifically, for customers of the first customer type (ie, new customers), there is no transaction record, that is, the system cannot obtain transaction data. Therefore, customers who may have ACH Reversals can be avoided through the basic information of the customers.

[0096] The basic information of a customer may include the customer's strongly associated attribute information and weakly associated attribute information.

[0097] Among them, the customer's strongly associated attribute information may include: ① ID number; ② mobile phone number; ③ email address; ④ terminal device used; ⑤ bank card information used when binding the bank card, including: bank card number and bank; ⑥ IP address, etc.

[0098] The weakly associated attribute information of a customer may include: ① age range; ② residential address; ③ work unit; ④ occupation; ⑤ relationship chain (i.e., relationship with the inviter), etc.

[0099] After a customer opens an account, the customer's strong correlation attribute information and weak correlation attribute information will be recorded in the system (bank card information is recorded after the card is bound). Customer A can be regarded as a node. If customer A and customer B have the same strong correlation attribute, then a line can be connected between customer A and customer B. At the same time, if customer A and customer C have the same strong correlation attribute, then a line can also be connected between customer A and customer C. In this way, customers with the same strong correlation attribute form a graph, which can be called a group. Therefore, all customers who open accounts with a brokerage firm can be divided into several groups, and it can be guaranteed that there are no duplicate customers between each two groups.

[0100] For example, Figure 2 As shown, customer A and customer B have the same ID number, customer A and customer C use the same terminal device, customer C and customer D use the same terminal device, and customer C and customer E have the same email address. Then customer A, customer B, customer C, customer D and customer E can be the first customer group.

[0101] Similarly, the system can construct a new Group based on the customer's weakly associated attribute information.

[0102] For example, Figure 3 As shown, customer a and customer b are in the same age range, customer a and customer c have the same residential address, customer c and customer d have the same occupation, and customer d and customer e work in the same unit, then customer a, customer b, customer c, customer d and customer e can be regarded as the second customer group.

[0103] In one embodiment, determining the credit funds to be granted to the customer based on the pre-built first credit model may include:

[0104] If any customer in the first customer group is confirmed to be a bad customer, no credit will be granted to all customers in the first customer group;

[0105] If any customer in the second customer group is confirmed to be a bad customer, whether to grant credit to the remaining customers in the second customer group is determined based on the staining rate;

[0106] If the staining rate of the second customer group is greater than a preset threshold, and the remaining customers in the second customer group are all potential bad customers, no credit will be granted to any customers in the second customer group.

[0107] Specifically, if the customer is a customer in the first customer group, such as Figure 2 In the example, customer A, customer B, customer C, customer D and customer E form a first customer group (which may be all or part of the first customer group) because there is a same strong attribute association relationship between any two of them.

[0108] A bad customer is one who has ACH Reversal and caused losses to the brokerage firm. This type of customer is defined as a black seed customer, i.e. a bad customer. For example, Figure 2 Customer A. Figure 4 As shown, the black seed customer can be colored yellow (indicated by shading in the figure). The colored customer in the figure is a bad customer. Since customers B and C connected to customer A are both strongly associated, customers B and C are also colored yellow (indicated by shading in the figure). The coloring process for customer A is applied to customers connected to customers B and C. Since all customers in this group are strongly associated, all customers in the group are colored (the coloring of all customers is not shown in the figure), that is, all customers in the group are defined as bad customers.

[0109] Therefore, if any customer in the first customer group is identified as a bad customer, no credit will be granted for deposits initiated by all customers in the first customer group, that is, the credit cash will be 0, so as to reduce the losses of the brokerage firm.

[0110] If the customer is a customer in the second customer group, such as Figure 3In the example, customer a, customer b, customer c, customer d, and customer e, because there is a same weak attribute relationship between any two of them, constitute a second customer group (which can be all or part of the second customer group). Figure 3 If customer a in the second customer group experiences an ACH Reversal and causes losses to the brokerage firm, customer a is defined as a black seed customer, or a bad customer. The staining rate for the second customer group is calculated. If the staining rate for the second customer group is greater than a preset threshold, all other unstained customers in the second customer group are considered potential bad customers, and no credit is granted to any potential bad customers when they deposit funds, i.e., the credit amount is zero. If the staining rate for the second customer group is less than or equal to the preset threshold, all other unstained customers in the second customer group are considered non-bad customers, and credit can be granted to them. It should be noted that the preset threshold can be set according to actual needs; for example, the preset threshold is set to 20%.

[0111] The dyeing rate of the second customer group can be calculated by the following example or by other methods.

[0112] In one embodiment, the dyeing rate of the second customer group is determined by the following steps:

[0113] Get the number of dyed customers and the number of undyed customers in the second customer group;

[0114] Obtain the weights of undyed customers associated with dyed customers through weakly associated attribute information;

[0115] Determine the dyeing rate based on the number of dyed customers, the number of undyed customers, and the weight.

[0116] Specifically, the weights corresponding to the various attributes in the weakly associated attribute information can be set according to actual experience or needs. For example, the weight corresponding to the customer's age range is set to 0.1, the weight corresponding to the customer's residential address is set to 0.8, the weight corresponding to the customer's work unit is set to 0.5, the weight corresponding to the customer's occupation is set to 0.1, the weight corresponding to the customer's relationship chain is set to 0.5, and the weight of a colored customer, i.e. a bad customer, is set to 1, etc.

[0117] According to the number of dyed customers, the number of undyed customers, and the weight, the dyeing rate is determined as follows:

[0118] Coloring rate = (coloring customer weight + uncoloring customer weight) / total number of customers in the second customer group

[0119] The weight of a dyed customer is the sum of the weights of all dyed customers in the second customer group, that is, the number of dyed customers. The weight of an undyed customer is the sum of the weights of all undyed customers connected to a dyed customer. The total number of customers in the second customer group is the sum of the number of all dyed customers and the number of all undyed customers.

[0120] For example, refer to Figure 3 , assuming that customer a is defined as a bad customer, that is, the weight of customer a is 1, customer a and customer b have the same age range, then the weight of customer b is 0.1, customer a and customer c have the same residential address, then the weight of customer c is 0.8, customer d and customer e have no connection with customer a, that is, the same weakly associated attribute information, so the weight of customer d and customer e is 0, therefore, the staining rate = (1+0.1+0.8) / (1+4) = 38%.

[0121] Assume that the preset threshold is set to 20%. Since 38%>20%, the remaining unstained customers in the second customer group are all potential bad customers and are not granted credit.

[0122] For another example, suppose that the second customer group includes 10 customers, one of whom is customer k, who is defined as a bad customer. The weight of customer k is 1. Customer k has the same occupation as customer m, so the weight of customer m is 0.1. Customer k and customer n have the same work unit, so the weight of customer n is 0.5. The remaining customers do not have the same weakly associated attributes as customer k. Therefore, the coloring rate = (1+0.1+0.5) / 10 = 16%.

[0123] Assume that the preset threshold is set to 20%. Since 16% is less than 20%, the remaining unstained customers in the second customer group are not bad customers, and credit is granted to the remaining unstained customers in the second customer group.

[0124] If the customer is a second-type customer, that is, an old customer, the credit cash granted to the old customer is essentially a loan model. The event of the customer depositing money and the platform granting credit to the customer can be compared to a loan behavior, except that no loan interest is charged at present. The customer will theoretically return this interest-free loan to the company on the 5th trading day after the deposit, that is, T+4, and the customer's actual funds will also be credited at this time.

[0125] In one embodiment, the second credit model is constructed by the following steps:

[0126] Obtain the effective net assets, risk tolerance and repayment willingness of the second type of customers;

[0127] Determine the customer's cash credit limit based on the customer's effective net assets, risk tolerance, and willingness to repay;

[0128] Construct a second credit model based on the customer's credit cash limit, global credit limit and customer's funds in transit.

[0129] Specifically, if existing customers already have assets on the platform (such as stocks and / or cash), the credit granted to these customers is a secured loan. The customer's existing funds serve as collateral, and when the customer defaults, the collateral can be used to repay the loan. The value of the collateral can be referred to as the customer's effective net equity. It should be noted that effective net equity refers to the customer's actual net assets, excluding funds in transit.

[0130] Since a customer's collateral consists of stocks and cash, not fixed assets, its collateral value must be calculated based on the customer's historical behavior. This conversion rate can be called the customer's risk tolerance (Resistance Rate). It should be noted that customer risk tolerance can refer to the collateralization ratio of the customer's collateral assets, that is, the maximum possible loss ratio of the customer's collateral assets, and its value can be (0,1).

[0131] At the same time, an ACH Reversal by a customer can be considered a failure to repay within the agreed-upon timeframe, a delinquency. The probability of this customer becoming delinquent can be called the customer's willingness to repay (WOR). It's important to note that the WOR calculates the probability of replenishing funds after another reversal for a customer who has already had a reversal. Its value can range from 0 to 1. As you can understand, if a customer has not had a reversal, this value is 1.

[0132] The customer's maximum credit cash limit (Max Credit Cash) may refer to the maximum credit cash limit that can be granted to a customer.

[0133] Since the above-mentioned customer's effective net assets, customer's risk tolerance and customer's willingness to repay rate are all linearly related to the customer's credit cash, therefore, based on the customer's effective net assets, customer's risk tolerance and customer's willingness to repay rate, the customer's credit cash limit is determined as follows:

[0134] Customer credit cash limit = customer's effective net assets × customer's risk tolerance × customer's repayment willingness rate.

[0135] The Global Provisional Cash Cap (Maximum Provisional Cash) is the maximum amount of cash allowed per client on the platform. This limit is determined by the brokerage firm's average assets.

[0136] Pending Deposit refers to the total deposit amount of a customer in the past 5 trading days (which can be set based on actual circumstances). Since deposits are currently determined based on experience, if no reversal occurs on the 5th trading day after the deposit, it means that the deposit is likely to be received.

[0137] Based on the customer's credit cash limit, global credit limit, and customer's funds in transit, the second credit model is constructed to determine the provisional cash as follows:

[0138] Credit cash = Min (customer credit cash limit, global credit limit, customer funds in transit).

[0139] In one embodiment, a client's effective net worth may be determined as follows:

[0140] Obtain the client's actual net worth;

[0141] Determine the client's effective net assets based on the client's actual net assets.

[0142] Specifically, as can be seen above, the maximum credit cash limit (Max Credit Cash) will not exceed the customer's effective net assets. For example, if a customer has a net asset of $50,000 and deposits $10,000, the maximum credit cash the platform can provide to the customer will also be $50,000. This is because, in theory, if all of the customer's funds in transit are reversed, the maximum loss the customer can withstand is their current actual net assets. Therefore, the customer's effective net assets are the amount of assets that can be pledged for loss.

[0143] At the same time, if the customer's net assets are negative, then the customer has no collateral to cover losses, and their effective net assets are 0. In summary, the formula for calculating the customer's effective net assets is as follows:

[0144] Customer's effective net assets = Max (customer's actual net assets, 0)

[0145] In one embodiment, the risk tolerance of a client may be determined by:

[0146] Obtain the maximum asset mortgage ratio, global leverage ratio, total market value of long stock positions, total market value of short stock positions, and client remaining cash;

[0147] The leverage ratio at the end of the day is determined based on the total market value of long stock positions, the total market value of short stock positions, and the client's remaining cash;

[0148] Determine the average leverage ratio for customers based on the end-of-day leverage ratio and the number of trading days;

[0149] Determine the customer's risk tolerance based on the customer's average leverage ratio, maximum asset mortgage ratio and global leverage coefficient.

[0150] Specifically, the maximum asset mortgage ratio is the maximum mortgage ratio of any customer's current assets.

[0151] The global leverage factor is used to limit the weight of the average leverage ratio in calculating the ability to combat risk.

[0152] Long Equity Market Value = Σ Long Stock Price × Number of Long Stock Shares.

[0153] Short Equity Market Value = Σ Short Equity Price × Number of Short Equity Shares (negative).

[0154] When the customer's remaining cash (Total Trade Balance) is negative, it means that the customer has financing.

[0155] Total Market Value = Total Market Value of Long Stock Holdings + |Total Market Value of Short Stock Holdings|;

[0156] Net Margin Equity = Customer's remaining cash + Total market value of long stock positions + Total market value of short stock positions;

[0157] End-of-day leverage = total market value / net margin equity.

[0158] It should be noted that if Total Market Value = Net Margin Equity = 0, this indicates that the client's net assets are zero and this data is not valuable for reference and will be discarded. If Net Margin Equity = 0 and Total Market Value > 0, the end-of-day leverage ratio can be set to 999 or any other value; there are no restrictions here. If Net Margin Equity < 0, this indicates that the client is in a loss at the end of the day, and the end-of-day leverage ratio can be set to 999 or any other value; there are no restrictions here.

[0159] The average leverage of a customer can be calculated as the arithmetic average of the leverage of the account at the end of the last N trading days:

[0160]

[0161] Among them, L i is the leverage ratio on the i-th trading day.

[0162] It should be noted that if a client's valid end-of-day data is less than N days, the maximum of all end-of-day leverage ratios for that client will be used. If a client discards data within the last N days, the maximum of all end-of-day leverage ratios for that client will be used.

[0163] A client's risk tolerance can be calculated based on their current leverage ratio. The higher the client's leverage ratio, the higher the risk of their current assets, the higher the possibility of loss, and the lower their risk tolerance. Conversely, the smaller the client's leverage ratio, the higher their risk tolerance.

[0164] Customer risk tolerance = Min (maximum asset mortgage ratio, global leverage factor / customer average leverage ratio).

[0165] In one embodiment, the customer's willingness to repay rate can be determined by:

[0166] Obtain the number of normal and received deposits, the number of recalled deposits within the due date, the number of recalled deposits overdue, and the total amount of deposits received by the user;

[0167] Determine the actual deposit efficiency based on the total deposit amount received, the number of recalls for non-overdue deposits, and the number of recalls for overdue deposits;

[0168] Determine the customer's willingness to repay based on the actual deposit efficiency, the number of normal and received deposits, and the number of overdue deposit recalls.

[0169] Specifically, all deposits of customers can usually be divided into four categories: normal and received deposits, recalled deposits within the due date (Reversal), recalled deposits overdue (Reversal), and deposits in transit (not used in this embodiment).

[0170] Assume that the number of normal deposits is N, the number of reversals for deposits that are not overdue is B, and the number of reversals for overdue deposits is O.

[0171] A non-overdue deposit reversal occurs when, after a deposit reversal, the customer makes another deposit within five trading days, and the total actual deposit amount exceeds the Max ($30, the deposit amount for this reversal), meaning the customer completes the repayment after the overdue period. This deposit reversal is considered a non-overdue deposit reversal. An overdue deposit reversal is considered a non-overdue deposit reversal if, after a reversal, the customer fails to initiate a valid deposit repayment within five trading days, meaning the deposit is actually overdue.

[0172] The actual deposit efficiency refers to the percentage of the customer's actual deposit amount to the total deposit amount, where the actual deposit amount is the total deposit amount minus the historical reversal deductions.

[0173] E = (total deposit amount - 30 × (O + B)) / total deposit amount.

[0174] It should be noted that in actual operations, each ACH Reversal transaction costs the customer $30, so 30 is used as the multiplier here; E≤1.

[0175] Customer repayment willingness rate R:

[0176] R=Max((1-(K×O) / (N+O)), 0).

[0177] Among them, the weight K=1-E.

[0178] like Figure 5 FIG. 5 is a schematic diagram of a structure of a credit deposit device 500 provided by an embodiment of the present invention. Figure 5 As shown, the device can be implemented as Figure 1 The method shown, the apparatus may include:

[0179] The acquisition module 510 is used to obtain the net assets, deposit and withdrawal records, and transaction records of the customer who initiated the deposit;

[0180] A first determination module 520 is used to determine the customer type of the customer based on the customer's net assets, deposit and withdrawal records, and transaction records;

[0181] The second determining module 530 is configured to determine the credit funds to be granted to the customer based on a pre-built credit model corresponding to the customer type.

[0182] Optionally, the first determining module 520 may also be configured to:

[0183] If the customer's net assets are 0 and the customer has no deposit or withdrawal records and no transaction records, the customer is determined to be the first customer type;

[0184] Otherwise, the customer is determined to be the second customer type.

[0185] Optionally, the second determining module 530 may also be configured to:

[0186] If the customer type is the first customer type, determining the credit funds to be granted to the customer based on a pre-established first credit model, wherein the first credit model is a credit model corresponding to the first customer type;

[0187] If the customer type is the second customer type, the credit funds to be granted to the customer are determined based on a pre-built second credit model, wherein the second credit model is a credit model corresponding to the second customer type.

[0188] Optionally, the second determining module 530 is further configured to:

[0189] Obtaining strongly associated attribute information and weakly associated attribute information of customers of the first customer type;

[0190] All customers of the first customer type who have the same strongly correlated attribute information are grouped as the first customer group;

[0191] The second customer group includes all customers of the first customer type who have the same weakly correlated attribute information as the customers in the first customer group.

[0192] The first customer group or the second customer group is used as the first credit model.

[0193] Optionally, the second determining module 530 is further configured to:

[0194] If any customer in the first customer group is confirmed to be a bad customer, no credit will be granted to all customers in the first customer group;

[0195] If any customer in the second customer group is confirmed to be a bad customer, whether to grant credit to the remaining customers in the second customer group is determined based on the staining rate;

[0196] If the staining rate of the second customer group is greater than a preset threshold, no credit will be granted to all customers in the second customer group.

[0197] Optionally, the second determining module 530 is further configured to:

[0198] Get the number of dyed customers and the number of undyed customers in the second customer group;

[0199] Obtain the weights of undyed customers associated with dyed customers through weakly associated attribute information;

[0200] Determine the dyeing rate based on the number of dyed customers, the number of undyed customers, and the weight.

[0201] Optionally, the second determining module 530 is further configured to:

[0202] Obtain the effective net assets, risk tolerance and repayment willingness of the second type of customers;

[0203] Determine the customer's cash credit limit based on the customer's effective net assets, risk tolerance, and willingness to repay;

[0204] Construct a second credit model based on the customer's credit cash limit, global credit limit and customer's funds in transit.

[0205] Optionally, the second determining module 530 is further configured to:

[0206] Obtain the client's actual net worth;

[0207] Determine the client's effective net assets based on the client's actual net assets.

[0208] Optionally, the second determining module 530 is further configured to:

[0209] Obtain the maximum asset mortgage ratio, global leverage ratio, total market value of long stock positions, total market value of short stock positions, and client remaining cash;

[0210] The leverage ratio at the end of the day is determined based on the total market value of long stock positions, the total market value of short stock positions, and the client's remaining cash;

[0211] Determine the average leverage ratio for customers based on the end-of-day leverage ratio and the number of trading days;

[0212] Determine the customer's risk tolerance based on the customer's average leverage ratio, maximum asset mortgage ratio and global leverage coefficient.

[0213] Optionally, the second determining module 530 is further configured to:

[0214] Obtain the number of normal and received deposits, the number of recalled deposits within the due date, the number of recalled deposits overdue, and the total deposit amount received by the customer;

[0215] Determine the actual deposit efficiency based on the total deposit amount received, the number of recalls for non-overdue deposits, and the number of recalls for overdue deposits;

[0216] Determine the customer's willingness to repay based on the actual deposit efficiency, the number of normal and received deposits, and the number of overdue deposit recalls.

[0217] The deposit credit granting device provided in this embodiment can execute the embodiment of the above method. Its implementation principle and technical effects are similar and will not be described in detail here.

[0218] Figure 6 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 6 , which shows a structural diagram of an electronic device 600 suitable for implementing an embodiment of the present application.

[0219] like Figure 6As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage unit 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the system 600 are also stored in the RAM 603. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0220] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, and the like; an output section 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or a modem. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 610 as needed, so that computer programs read therefrom can be installed into the storage section 608 as needed.

[0221] In particular, according to the embodiments of the present disclosure, the above reference Figure 1 The described process can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for executing the above-described deposit credit authorization method. In such an embodiment, the computer program can be downloaded and installed from a network via the communication component 609 and / or installed from removable media 611.

[0222] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0223] The units or modules involved in the embodiments described in this application may be implemented by software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0224] As another aspect, the present application further provides a computer-readable storage medium. This computer-readable storage medium may be the computer-readable storage medium included in the aforementioned apparatus in the above-mentioned embodiment, or may be a standalone computer-readable storage medium not incorporated into the apparatus. The computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the deposit credit authorization method described in the present application.

[0225] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in this application.

Claims

1. A deposit credit method, characterized in that: The method includes: Obtain the net assets, deposit and withdrawal records, and transaction records of the client who initiated the deposit; Determine the customer type based on the customer's net assets, deposit and withdrawal records, and transaction records; Determining the credit funds to be granted to the customer based on a pre-built credit model corresponding to the customer type; Determining the customer type of the customer based on the customer's net assets, deposit and withdrawal records, and transaction records includes: If the customer's net assets are 0 and the customer has no deposit or withdrawal records and no transaction records, the customer is determined to be a first customer type; Otherwise, determining that the customer is a second customer type; The step of determining the credit funds to be granted to the customer based on a pre-built credit model corresponding to the customer type includes: If the customer type is the first customer type, determining the credit funds to be granted to the customer based on a pre-established first credit model, wherein the first credit model is a credit model corresponding to the first customer type; If the customer type is the second customer type, determining the credit funds to be granted to the customer based on a pre-established second credit model, wherein the second credit model is a credit model corresponding to the second customer type; The first credit model is constructed through the following steps: Obtaining strongly associated attribute information and weakly associated attribute information of a customer of the first customer type; wherein the strongly associated attribute information of the customer includes at least one of the following: ID number, mobile phone number, email address, terminal device used, bank card information used when binding a bank card, and IP address, wherein the bank card information used when binding a bank card includes the bank card number and the bank to which it belongs; and the weakly associated attribute information of the customer includes at least one of the following: age range, residential address, workplace, occupation, and relationship with the inviter; Any customer with the same strongly correlated attribute information among all customers of the first customer type is used as a first customer group; Any customer with the same weakly associated attribute information among the remaining customers of the first customer type except the customers in the first customer group is used as a second customer group; Using the first customer group or the second customer group as the first credit model; The step of determining the credit funds to be granted to the customer based on the pre-built first credit model includes: If any of the customers in the first customer group is determined to be a bad customer, no credit will be granted to any of the customers in the first customer group; a bad customer is a customer who has experienced an ACH Reversal and has caused losses to the brokerage firm; If any of the customers in the second customer group is confirmed to be a bad customer, determining whether to grant credit to the remaining customers in the second customer group based on the staining rate; If the staining rate of the second customer group is greater than a preset threshold, no credit is granted to all customers in the second customer group; The method further comprises: Get the number of dyed customers and the number of undyed customers in the second customer group; Obtaining the weight of the unstained customer associated with the stained customer through weakly associated attribute information; The dyeing rate is determined according to the number of the dyed customers, the number of the undyed customers, and the weight.

2. The deposit credit granting method according to claim 1, characterized in that: The second credit model is constructed through the following steps: Obtain the effective net assets, risk tolerance, and repayment willingness of the second type of customers; Determine the customer's cash credit limit based on the customer's effective net assets, the customer's risk tolerance and the customer's willingness to repay; The second credit model is constructed based on the customer's credit cash limit, the global credit limit and the customer's funds in transit.

3. The deposit credit granting method according to claim 2, characterized in that: The method further includes: Obtaining the actual client net worth of said client; Determine the effective net assets of the client based on the client's actual net assets.

4. The deposit credit granting method according to claim 3, characterized in that: The method further includes: Obtain the maximum asset mortgage ratio, global leverage ratio, total market value of long stock positions, total market value of short stock positions, and client remaining cash; Determine the end-of-day leverage ratio based on the total market value of the long stock positions, the total market value of the short stock positions, and the client's remaining cash; Determine the average leverage ratio for clients based on the end-of-day leverage ratio and the number of trading days; The risk tolerance of the client is determined based on the client's average leverage ratio, the maximum asset-collateral ratio, and the global leverage coefficient.

5. The deposit credit granting method according to claim 2, characterized in that: The method further includes: Obtain the number of normal and received deposits, the number of recalled deposits that are not overdue, the number of recalled deposits that are overdue, and the total deposit amount received by the customer; Determine the actual deposit efficiency based on the total deposit amount received, the number of recalls for non-overdue deposits, and the number of recalls for overdue deposits; The customer's willingness to repay rate is determined based on the actual deposit efficiency, the number of normal and received deposits, and the number of overdue deposit recalls.

6. A deposit credit device, characterized in that: The device includes: The acquisition module is used to obtain the net assets, deposit and withdrawal records, and transaction records of the customer who initiated the deposit; A first determination module is used to determine the customer type of the customer based on the customer's net assets, deposit and withdrawal records, and transaction records; A second determining module is configured to determine the credit funds to be granted to the customer based on a pre-built credit model corresponding to the customer type; Determining the customer type of the customer based on the customer's net assets, deposit and withdrawal records, and transaction records includes: If the customer's net assets are 0 and the customer has no deposit or withdrawal records and no transaction records, the customer is determined to be a first customer type; Otherwise, determining that the customer is a second customer type; The step of determining the credit funds to be granted to the customer based on a pre-built credit model corresponding to the customer type includes: If the customer type is the first customer type, determining the credit funds to be granted to the customer based on a pre-established first credit model, wherein the first credit model is a credit model corresponding to the first customer type; If the customer type is the second customer type, determining the credit funds to be granted to the customer based on a pre-established second credit model, wherein the second credit model is a credit model corresponding to the second customer type; The first credit model is constructed through the following steps: Obtaining strongly associated attribute information and weakly associated attribute information of a customer of the first customer type; wherein the strongly associated attribute information of the customer includes at least one of the following: ID number, mobile phone number, email address, terminal device used, bank card information used when binding a bank card, and IP address, wherein the bank card information used when binding a bank card includes the bank card number and the bank to which it belongs; and the weakly associated attribute information of the customer includes at least one of the following: age range, residential address, workplace, occupation, and relationship with the inviter; Any customer with the same strongly correlated attribute information among all customers of the first customer type is used as a first customer group; Any customer with the same weakly associated attribute information among the remaining customers of the first customer type except the customers in the first customer group is used as a second customer group; Using the first customer group or the second customer group as the first credit model; The step of determining the credit funds to be granted to the customer based on the pre-built first credit model includes: If any of the customers in the first customer group is determined to be a bad customer, no credit will be granted to any of the customers in the first customer group; a bad customer is a customer who has experienced an ACH Reversal and has caused losses to the brokerage firm; If any of the customers in the second customer group is confirmed to be a bad customer, determining whether to grant credit to the remaining customers in the second customer group based on the staining rate; If the staining rate of the second customer group is greater than a preset threshold, no credit is granted to all customers in the second customer group; The device further comprises: Get the number of dyed customers and the number of undyed customers in the second customer group; Obtaining the weight of the unstained customer associated with the stained customer through weakly associated attribute information; The dyeing rate is determined according to the number of the dyed customers, the number of the undyed customers, and the weight.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the deposit credit method according to any one of claims 1 to 5 is implemented.

8. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the deposit credit authorization method as described in any one of claims 1 to 5 is implemented.

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