CN118096362A - Transaction service processing method and device - Google Patents

Abstract

The present application relates to a transaction service processing method, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: acquiring a service processing request of a target transaction service pair for specifying a transaction currency pair; inquiring a price adding coefficient of the appointed transaction currency pair of the target transaction service in response to the service processing request, wherein the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the appointed transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor; determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate; and executing the business processing request based on the exchange rate quotation. The method can improve the service utilization rate.

Description

Transaction service processing method and device

Technical Field

The present application relates to the field of data processing technology, and in particular, to a transaction service processing method, apparatus, computer device, storage medium, and computer program product.

Background

Since cross-border trade settlement involves exchange rates that have volatility, payment institutions are at risk of losing exchange rate changes when processing refund business.

In this case, the payment mechanism usually avoids the damage risk caused by change of exchange rate by means of price adding or charging of exchange rate when clients inquire. However, several methods cannot flexibly cope with actual services, which results in low service usage.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a service processing method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve service usage.

In a first aspect, the present application provides a transaction service processing method. The method comprises the following steps:

acquiring a service processing request of a target transaction service pair for specifying a transaction currency pair;

Inquiring a price adding coefficient of the appointed transaction currency pair of the target transaction service in response to the service processing request, wherein the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the appointed transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor;

Determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate;

and executing the business processing request based on the exchange rate quotation.

In a second aspect, the application also provides a transaction service processing device. The device comprises:

The business request module is used for acquiring a business processing request of a target transaction business pair designated transaction currency pair;

The price adding processing module is used for responding to the service processing request and inquiring a price adding coefficient of the appointed transaction currency pair of the target transaction service, wherein the price adding coefficient is obtained by calculation based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the appointed transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor;

The quotation module is used for determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate;

and the business processing module is used for executing the business processing request based on the exchange rate quotation.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:

acquiring a service processing request of a target transaction service pair for specifying a transaction currency pair;

Inquiring a price adding coefficient of the appointed transaction currency pair of the target transaction service in response to the service processing request, wherein the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the appointed transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor;

Determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate;

and executing the business processing request based on the exchange rate quotation.

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

acquiring a service processing request of a target transaction service pair for specifying a transaction currency pair;

Inquiring a price adding coefficient of the appointed transaction currency pair of the target transaction service in response to the service processing request, wherein the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the appointed transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor;

Determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate;

and executing the business processing request based on the exchange rate quotation.

In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:

acquiring a service processing request of a target transaction service pair for specifying a transaction currency pair;

Inquiring a price adding coefficient of the appointed transaction currency pair of the target transaction service in response to the service processing request, wherein the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the appointed transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor;

Determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate;

and executing the business processing request based on the exchange rate quotation.

According to the transaction service processing method, the device, the computer equipment, the storage medium and the computer program product, the current original exchange rate is weighted by the weighting coefficient to obtain the exchange rate quotation of the appointed transaction currency pair of the target transaction service, so that the finally obtained exchange rate quotation is provided with an added price on the basis of the original exchange rate, the added price is calculated based on at least one of the predicted refund proportion of the target transaction service in the supported maximum refund period and the exchange rate fluctuation rate of the appointed transaction currency pair, the exchange rate fluctuation rate and the predicted refund proportion are positively correlated with the added price coefficient, the exchange rate quotation can be different from the traditional fixed exchange rate added price mode, and can be flexibly adjusted according to the predicted refund proportion of the target transaction service and/or the exchange rate fluctuation rate condition of the transaction currency, so that users can be attracted to use the target transaction service, and the service utilization rate is improved.

Drawings

FIG. 1 is an application environment diagram of a transaction service processing method in one embodiment;

FIG. 2 is a flow chart of a transaction service processing method in one embodiment;

FIG. 3 is a flowchart illustrating steps in one embodiment of a method for determining a predicted refund proportion of a target transaction for a supported maximum refund period;

FIG. 4 is a flowchart illustrating steps for obtaining target historical refund data and target historical transaction data associated with a target transaction business during a maximum refund period in one embodiment;

FIG. 5 is a flowchart illustrating steps for predicting a predicted refund ratio for a target transaction based on target historical refund data and target historical transaction data in one embodiment;

FIG. 6 is a flow chart illustrating steps in one embodiment of a method for determining a valence coefficient;

FIG. 7 is a flowchart illustrating steps for determining a valence coefficient according to another embodiment;

FIG. 8 is a flow chart of a transaction processing method according to another embodiment;

FIG. 9 is a block diagram of a transaction service processing device in one embodiment;

fig. 10 is an internal structural view of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The business processing method provided by the embodiment of the application can be applied to the application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on the cloud or other servers. The terminal 102 triggers a service processing request for the designated transaction currency pair to a server, and the server acquires the service processing request for the target transaction service for the designated transaction currency pair; inquiring a price adding coefficient of a designated transaction currency pair of a target transaction service in response to a service processing request, wherein the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the designated transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor; determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate; the business process request is performed based on the rate offer. The terminal 102 may be, but not limited to, various desktop computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In one embodiment, as shown in fig. 2, a transaction service processing method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:

step 202, obtaining a service processing request of a target transaction service pair designated transaction currency pair.

The transaction platform can be a transaction platform for providing commodities, financial assets, virtual articles and the like. Generally, a transaction platform can provide various types of services. Taking a trading platform of financial assets as an example, trading of different types of businesses such as bonds, funds, stocks, and the like can be provided.

Under different types of transactions, if the transaction service only supports a specific currency as the transaction currency, and when the transaction currency is inconsistent with the currency held by the user, exchange rate conversion is involved, and the holding currency needs to be converted into the transaction currency for transaction. Thus, the specified transaction currency pair may include (holding currency: transaction currency). For example, for a trade coin of U.S. money, the coin held by the user is a harbor coin, and the designated trade coin pair may be (U.S. money: harbor coin). It will be appreciated that when the currency held by the user is consistent with the transaction currency, the transaction may be conducted directly as no exchange rate conversion is involved. When the currency held by the user is inconsistent with the transaction currency, the transaction business processing method is adopted for processing.

The transaction service can be configured to only support unique transaction currencies, can also be configured to multiple transaction currencies, when the transaction service is configured to support multiple transaction currencies, the processing interface of the target transaction service displays the price data of default transaction currency pairs after exchange rate conversion, and a user opens the processing interface of the target transaction service and can also reselect one of the supported transaction currency pairs.

The method for acquiring the currency held by the user can comprise the following cases:

first case: when the held coin types are registered in the user personal information, the held coin types of the user are acquired from the user personal information.

Second case: when the user has a history transaction record, the currency held by the user is obtained from the history transaction record.

Third case: when the user personal information does not register the held currency and there is no history transaction record of the user, the currency held by the user is estimated based on the user's IP address, the presentation language of the web page, and the like.

The service processing request may be an opening request of a processing interface of the target transaction service, which is triggered by a user.

Step 204, in response to the service processing request, inquiring a price adding coefficient of a designated transaction currency pair of the target transaction service, wherein the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation rate of the designated transaction currency pair; the exchange rate fluctuation rate and the predicted refund proportion are positively correlated with the price coefficient.

Wherein the price adding coefficient is a regulating parameter for carrying out price adding processing on the basic exchange rate. For payment institutions, to avoid the exchange rate loss risk, the industry is mostly controlled in three ways.

The first way is: limiting the refund period. This approach is achieved by limiting a shorter refund period. For example, a transaction platform may only support a 30 minute pick-up transaction and a full refund, over 30 minutes, and typically does not provide a refund. This approach typically only supports users canceling transactions in a short period of time, refunds at the exchange rate at the time of the transaction, and such an approach cannot flexibly meet the refund needs of the users, affects the user experience, and may lose the portion of the order that requires potentially longer refund cycles.

The second way is: and collecting the commission. This approach does not support the original rate refund, and reexchange time rate is used at reexchange and a fee is charged. In the refund process, the user can strongly perceive that the refund behavior is paid, so that the user experience is poor.

Third mode: and (5) rate adding. For example, a certain trading platform refunds with a bank price, which is generally updated slowly and higher than the instant exchange rate of the foreign exchange market, and thus corresponds to risk control by adding price using the exchange rate. If the method adopts an empirical price adding mode, a user cannot obtain the optimal price, the price competitiveness is reduced, if the method adopts a bank price quotation mode, generally, the bank price updating frequency is lower, the off-shore market price cannot be reflected, and the quotation is easily deviated from the market.

Aiming at the defects of the traditional exchange rate damage risk avoidance method, the transaction service processing method provided by the application calculates the price adding coefficient in advance based on at least one of the predicted refund proportion of the target transaction service in the supported maximum refund period and the exchange rate fluctuation rate of the designated transaction currency pair. After the calculation is completed, a price adding coefficient table is constructed by taking the price adding coefficient as a key value according to the joint primary key of the (target transaction business, currency pair). And when the actual business is processed, responding to a business processing request, inquiring a price adding coefficient table by taking the target transaction business and the appointed transaction currency pair as an inquiry main key, and obtaining the price adding coefficient of the appointed transaction currency pair of the target transaction business.

The obtaining mode of the valence coefficient comprises the following cases:

First kind: and determining the price adding coefficient according to the predicted refund proportion of the target transaction service in the supported maximum refund period.

Second kind: and determining the price adding coefficient according to the exchange rate fluctuation rate of the designated transaction currency pair.

Third kind: and determining a price adding coefficient according to the predicted refund proportion of the target transaction business in the supported maximum refund period and the exchange rate fluctuation rate of the designated transaction currency pair.

The maximum refund period is the longest refund time interval supported by the target transaction service after the transaction is completed. Specifically, the difference between refund time and transaction time. Different transaction services may set different maximum refund periods depending on the service characteristics.

The refund proportion is the ratio of refund volume to transaction volume, and in one embodiment, the bigger the refund proportion, the bigger the remittance rate damage benefit risk, in order to avoid the remittance rate damage benefit risk that causes because of refund, the setting that can correspond is great to add price coefficient, and the smaller the refund proportion, the smaller the remittance rate damage benefit risk, the setting that can correspond is great to add price coefficient.

In one embodiment, because the exchange rate fluctuation rate and time are strongly related, the longer the maximum refund period is set, the greater the exchange rate damage risk, i.e., the different refund periods are different. Therefore, refund proportion in different refund periods can be respectively predicted, so that different remittance fluctuation risks can be applied to the different refund periods.

Meanwhile, exchange rate fluctuation rates of different currency pairs are also different, and if the currency pairs are not distinguished, the same price adding coefficient is set for all the transaction currency pairs, so that the influence is caused. For example, if the exchange rate of the currency pair is not large, if the addition coefficient is too large, a certain loss is caused to the user, whereas if the exchange rate of the currency pair is large, if the addition coefficient is too small, an exchange rate loss may be caused to the paymate. Therefore, in the case of the pair of currencies, it is necessary to flexibly adjust the price-adding coefficient according to the fluctuation rate of the pair of currencies.

Meanwhile, considering the exchange rate loss benefit risks of different refund periods and the exchange rate fluctuation rate conditions of different currency pairs, the price adding coefficient is calculated based on the predicted refund proportion of the target transaction business in the supported maximum refund period and the exchange rate fluctuation rate of the designated transaction currency pair, and the price adding coefficient can cover the exchange rate fluctuation rate risks of different refund periods.

Step 206, determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the addition coefficient and the current original exchange rate.

Specifically, the current original exchange rate is weighted by using a weighting coefficient to obtain an exchange rate quotation of a designated transaction currency pair of the target transaction service, so that the finally obtained exchange rate quotation has an added price based on the original exchange rate, the added price is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and an exchange rate fluctuation rate of the designated transaction currency pair, the exchange rate fluctuation rate and the predicted refund proportion are positively correlated with the added price coefficient, and if the predicted refund proportion is small, the corresponding added price coefficient is also smaller, so that a smaller added price coefficient can be determined to avoid exchange rate damage risk, and a competitive exchange rate is provided for users. If the predicted refund proportion is large, the corresponding price adding coefficient is also larger, so that the exchange rate damage risk can be avoided by determining a larger price adding coefficient, and the exchange rate damage risk of the payment platform is reduced.

In this embodiment, the rate quotation can be different from the conventional fixed rate pricing method, but can be flexibly adjusted according to the predicted refund proportion of the target transaction service and/or the rate fluctuation rate of the transaction currency, so that competitive rate is provided for the user while the damage risk of the rate of the payment platform is reduced.

At step 208, a business process request is performed based on the rate offer.

Specifically, after obtaining the exchange rate quotation of the target transaction service, the service platform performs service processing based on the exchange rate quotation according to the specific content of the target transaction service. For example, executing a business process request based on an exchange rate offer includes: according to the exchange rate quotation and the appointed trading currency, converting the user holding currency into the price information of the trading currency; and loading a processing interface of the target transaction service, and displaying the price information of the target transaction service according to the transaction currency.

According to the transaction service processing method, the current original exchange rate is weighted by the weighting coefficient to obtain the exchange rate quotation of the appointed transaction currency pair of the target transaction service, so that the finally obtained exchange rate quotation is provided with a price on the basis of the original exchange rate, the price is calculated based on at least one of the predicted refund proportion of the target transaction service in the supported maximum refund period and the exchange rate fluctuation rate of the appointed transaction currency pair, and the exchange rate fluctuation rate and the predicted refund proportion are positively correlated with the price adding coefficient, so that the exchange rate quotation can be different from the traditional fixed exchange rate price adding mode, and can be flexibly adjusted according to the predicted refund proportion of the target transaction service and/or the exchange rate fluctuation rate condition of the transaction currency, and therefore users can be attracted to use the target transaction service, and the service utilization rate is improved.

In another embodiment, the manner in which the predicted refund proportion of the target transaction traffic within the supported maximum refund period is determined, as shown in fig. 3, includes:

Step 302, obtaining target historical refund data and target historical transaction data associated with a target transaction business during a maximum refund period.

The maximum refund period supported by the target transaction service can be set by the transaction platform, and when the transaction is completed, the target transaction service only supports refund service in the maximum refund period. For example, if the maximum refund period supported by the target transaction service is 90 days, the target transaction service only supports refund service within 90 days of the transaction date, and if the refund date-transaction date is 90 days or less, refund can be successful.

The data sources of the target historical refund data and the target historical transaction data related to the target transaction business may be preconfigured. The historical refund data of the target transaction service in the maximum refund period can be configured as target historical refund data, and the historical transaction data of the target transaction service in the maximum refund period can be configured as target historical transaction data. Taking the target transaction service a as an example, if the configured maximum refund period is 90 days, refund data of the target transaction service a within 90 days is taken as target refund data, and transaction data of the target transaction service a within 90 days is taken as target transaction data.

The historical refund data of the target user group of the target transaction service in the maximum refund period can be configured as target historical refund data, and the transaction data of the target user group in the maximum refund period can be configured as target historical transaction data. Taking the target transaction service A as an example, determining a target user group of the target transaction service A in a service platform, taking historical refund data of the target user group in a maximum refund period of the service platform as target historical refund data of the target transaction service A, and taking the historical transaction data of the target user group in the maximum refund period of the service platform as target historical transaction data of the target transaction service A.

Step 304, predicting the predicted refund proportion of the target transaction business in different refund periods according to the target historical refund data and the target historical transaction data.

Wherein the predicted refund proportion is determined based on the target historical refund data and the target historical transaction data related to the target transaction service, and specifically, a ratio of the target historical refund data and the target historical transaction data may be taken as the predicted refund proportion.

It should be noted that, because the exchange rate fluctuation and the refund-transaction time interval are strongly related, in order to reflect the influence of different refund periods on the exchange rate damage risk, the refund interval periods may be sliced based on the historical transaction records to obtain a plurality of different refund periods, so as to predict refund proportions of the different refund periods, so as to facilitate different pricing for the different interval periods. For example, the weighting coefficient may be obtained by multiplying the predicted refund proportion of different interval periods by the price adding factor, so that the higher the predicted refund, the greater the contribution of the refund period to the weighting coefficient, and by setting the price adding coefficient, the remittance damage risk of the part can be avoided correspondingly.

In this embodiment, according to the historical refund data and the historical transaction data related to the target transaction service, refund rules of different refund periods are mined from the historical data, so that refund proportions of different refund periods are quantized, and further matched price adding coefficients can be determined by taking the refund proportions as references, so that a basis is provided for determining the price adding coefficients.

In another embodiment, the manner in which the exchange rate fluctuation rate for a given transaction currency pair is determined includes: determining exchange rate fluctuation rates of specified transaction currency pairs at different times in different periods according to historical exchange rate data of the specified transaction currency pairs in the maximum refund period; and determining the exchange rate fluctuation price adding factors according to the exchange rate fluctuation rates of the designated trading currencies at different times and at intervals in different periods.

Wherein volatility is the degree of fluctuation in the price of a financial asset and is a measure of uncertainty in the rate of return of the asset to reflect the risk level of the financial asset. The higher the volatility, the more severe the fluctuation of the financial asset price, the stronger the uncertainty of the asset profitability; the lower the volatility, the more gradual the fluctuation in the price of the financial asset and the more deterministic the asset return. Thus, the volatility is an important indicator of financial risk control.

The exchange rate fluctuation rate is specifically an exchange rate change case, wherein the exchange rate fluctuation rate can be calculated based on the fair exchange rate. The fair exchange rate refers to an exchange rate published by a bank or a financial structure, including buying and selling reference prices, which cannot be directly exchanged without transaction amount, and the update frequency is relatively lagged.

The traditional method for calculating the exchange rate fluctuation rate may only consider the exchange rate fluctuation condition of the previous day, and the method is not comprehensive.

In this embodiment, the exchange rate fluctuation rate of the designated transaction currencies at different times to the time intervals in different periods is calculated in the maximum refund period, so that the exchange rate change condition of the designated transaction currencies at different times to the parameters at different times can be determined. That is, the time reference for measuring the exchange rate fluctuation rate is not unique in this embodiment. In this embodiment, an interval time within a period may be set, and the rate change condition of the interval time within the period of the rate fluctuation may be calculated. For example, if the period interval is five days, it is necessary to calculate the fluctuation of the exchange rate of the previous day, the fluctuation of the exchange rate of the previous two days, the fluctuation of the exchange rate of the previous day, the fluctuation of the exchange rate of the previous four days, and the fluctuation of the exchange rate of the previous day, and the fluctuation of the exchange rate of the previous five days.

For example, taking T as the maximum transaction period, N as the interval time in the period, taking the fair exchange rate of historical N days, wherein the fair exchange rate in the interval of one day is taken in hours; the fair exchange rate of more than one day apart is obtained according to the day; the starting point of the picking up is the exchange rate at the moment 24:00:00 of the last day of the last month; the calculation of the fetch and interval period filters non-transacted days.

Exchange rate fluctuation rate sigma calculation rule: σ= |current day rate/N previous day rate-1|, N is the inter-slice period interval.

The fluctuation of exchange rate can also adopt logarithmic fluctuation, and for each time period, the natural logarithm of the ratio of the exchange rate at the end of the time period to the exchange rate at the end of the last time period is obtained; then, the standard deviation of these logarithmic values is obtained, and then multiplied by the square root of the number of time periods contained in a certain time zone, to obtain the historical fluctuation rate.

In one embodiment, the maximum refund cycle is 90, the predicted time point is 12 months and 1 day, and the calculated exchange rate fluctuation rate is shown in table 1.

TABLE 1 exchange Rate fluctuation Rate Table

Further, the rate fluctuation price adding factor may be determined according to the designated transaction currency at different times and the rate fluctuation price at intervals in different periods, and the rate fluctuation price adding factor may be determined by taking a set of rate fluctuation price covering most of the rate loss risk among the rate fluctuation price. Specifically, determining the rate fluctuation addition factor for the rate fluctuation rate at intervals in different periods according to the designated transaction currencies at different times comprises: and determining the exchange rate fluctuation price adding factor according to the exchange rate fluctuation rate of the designated transaction currency at different times and the time intervals in different periods.

For example, n=90 days, and the refund cycle interval length T is 5 days, the fluctuation rate σ is 90×5=450 points in total, and the rate fluctuation rate of the target α -bit in the 450 points is taken to determine the rate fluctuation addition factor. The alpha score may be empirically determined to cover a majority of the rate damage risk, e.g., taking 40 scores to cover about 80% of the rate damage according to a measurement, then the alpha score = 40% and taking the value of the location of 450 candidate rate fluctuations 40% as the rate fluctuation addition factor. In this embodiment, on the one hand, through the fluctuation rate measurement of the historical fair exchange rate, the exchange rate fluctuation risk faced by the payment platform in different refund periods can be applied, and on the other hand, the problem of the fluctuation rate difference of the exchange rates of different currency pairs is considered, so that the matched price adding coefficient can be determined for the different currency pairs.

In one embodiment, after determining the rate fluctuation price adding factor, the rate fluctuation price adding factor may be directly used as the price adding factor, however, since the rate fluctuation and the refund-transaction time interval are strongly related, different price adding needs to be performed on different interval periods, and thus the price adding factor may also be determined by combining the predicted refund proportion of different refund periods and the rate fluctuation price adding factor, for example, the price adding factor=Σ the predicted refund proportion of different refund periods. So that the price adding coefficient can be related to the exchange rate fluctuation, refund period and exchange rate damage risk.

In another embodiment, as shown in fig. 4, obtaining target historical refund data and target historical transaction data relating to a target transaction business during a maximum refund period includes:

step 402, determining the characteristics of the target object according to the historical transaction data of the target transaction service.

The operation behavior of the user is often related to the behavior characteristics of the user, and the behavior characteristics of the user are generally more stable, for example, if the user has more frequent refund records on other services of the service platform, the probability that the user refunds on the target transaction service can also be expected to be larger. Therefore, in this embodiment, the target user group of the target transaction service may be predicted, and the refund proportion of the target transaction service may be predicted from the refund behavior of the target user group on the service platform. The target object characteristics of the target transaction service are analyzed based on the existing historical transaction data of the target transaction service, and a clustering method can be adopted to determine the target object characteristics.

Step 404, determining a target user group according to the target object characteristics.

The target object characteristics of the target transaction service can be analyzed based on the existing historical transaction data of the target transaction service, and the target user group is determined based on matching of the target object characteristics among users of the service platform.

Step 406, determining historical refund data of the target user group in the maximum refund period in the service platform as target historical refund data, and determining historical transaction data of the target user group in the maximum refund period in the service platform as target historical transaction data.

Specifically, matching is performed among users of the service platform according to the characteristics of the target object, and a target user group is determined. For example, analyzing existing historical transaction data of a target transaction business, determining target object characteristics includes: the preference is home and the age is 18-45 years, and the target user group can be determined by searching among users of the service platform based on the characteristics. And further, the historical transaction data of the target user group in all business subclasses of the business platform in the maximum refund period are determined to be target historical transaction data, and the historical refund data of the target user group in all business subclasses of the business platform in the maximum refund period are determined to be target historical refund data.

In this embodiment, the refund proportion of the target transaction service is predicted according to the historical refund information of the target user group of the target transaction service, the target user group is predicted from the aspect of feature stability, and refund prediction is performed from the user layer, so that the refund prediction accuracy can be achieved.

In another embodiment, predicting the predicted refund proportion of the target transaction business based on the target historical refund data and the target historical transaction data, as shown in fig. 5, includes:

Step 502, determining a plurality of different refund periods according to the maximum refund period.

Wherein, although a maximum refund period is defined, the actual refund period of the transaction if refunds occur cannot be predicted in the inquiry phase. The remittance damage risk is strongly related to the refund period, so that a plurality of different refund periods can be set, for example, the time interval in the refund period can be set, and a plurality of different refund periods can be determined. For example, if the refund period time interval is 5 days, then the refund period may be 0, (0, 5), (5, 10), etc., where the period time interval determines the fine granularity of the refund ratio prediction, the period time interval should be matched to the maximum refund period, and should not be too large or too small.

The refund period is calculated by filtering non-trade days, the refund period larger than 1 day is required to be rounded off and is participated in the grouping by integers, and the refund period smaller than one day is required to be rounded off and is participated in the grouping by molecular integers/24; for example [0, 5) & [0,1/24 ].

And 504, taking the duty ratio of the target historical refund data in the target historical transaction data in each different refund period as the predicted refund proportion of the target transaction business refund period.

Wherein, the prediction period of the predicted refund proportion may be fixed, for example, the predicted refund proportion of the current month may be predicted based on the historical transaction data and the historical refund data of the past 90 days at the beginning of each month. Taking a service platform with a maximum refund period of 90 days as an example, if each refund period interval is 10, calculating a predicted refund proportion table of 12 months is shown in table 2.

Table 2 predicted refund proportion table

For the technical scheme that the price adding coefficient is determined according to the predicted refund proportion, the weighting coefficient of the predicted refund proportion of different slicing periods can be determined, for example, the more the slicing period is far from the transaction time, the larger the weighting coefficient is, so that different prices are added to the predicted refund of different slicing periods, the total predicted refund proportion is obtained, and the price adding coefficient is determined according to the predicted refund proportion of the part.

For the technical scheme that the price adding coefficient is determined together according to the predicted refund proportion and the remittance fluctuation price adding factor, a node of the business processing request cannot judge which specific refund period the transaction will fall on, so that the first price adding coefficient is obtained by accumulating weighted values of the refund proportion and the remittance fluctuation price adding factor of the slicing period, namely, the first weighted coefficient=Σ and the predicted refund proportion and the remittance fluctuation price adding factor of different refund periods, and different price adding can be carried out on different interval periods.

In this embodiment, the predicted refund proportion of the multiple different refund periods is obtained by performing slicing processing of the different refund periods in the maximum refund period, so that different refund periods can be charged differently, and different remittance rate fluctuation risks corresponding to the different refund periods are reflected.

In another embodiment, the manner in which the valence coefficients are determined, as shown in FIG. 6, comprises:

step 602, determining a first price adding parameter according to predicted refund proportion and exchange rate fluctuation price adding factors of the target transaction business in different refund periods, wherein the predicted refund proportion and the exchange rate fluctuation price adding factors are positively correlated with the first price adding parameter.

Specifically, the predicted refund proportion and the exchange rate fluctuation price adding factor of different refund periods are weighted, and a first price adding parameter is determined. That is, the larger the predicted refund proportion of the refund period, the greater the contribution to the first pricing parameter. The first price adding parameter can be flexibly adjusted according to the predicted refund remittance fluctuation condition and the predicted refund proportion of different refund periods through the predicted refund proportion and the remittance fluctuation price adding factor of different refund periods.

And the first price adding parameter is correlated with the predicted refund proportion and the price adding factor in a positive correlation way, if the predicted refund proportion of the refund period is smaller, the contribution to the first price adding parameter is smaller, so that the risk of losing the exchange rate can be avoided by determining a smaller first price adding parameter, and competitive exchange rate is provided for users. And under the condition of large exchange rate fluctuation, a larger exchange rate fluctuation rate is determined, so that the exchange rate damage risk of the payment platform can be reduced.

Step 604, determining the price adding coefficient according to the first price adding parameter.

In this embodiment, the price adding coefficient considers the refund proportion and exchange rate fluctuation conditions of different refund periods, predicts the refund proportion and possible damage and benefit caused by the refund proportion to the user history behavior, and provides the original exchange rate refund service for the client by adopting a dynamic price adjustment mode for different refund periods, so that the client is provided with more competitive exchange rate on the premise of ensuring user experience, the real foreign exchange market price is accurately and rapidly reflected, and the damage and benefit risk of the self exchange rate is controlled in a proper range by the risk price adding control.

However, in practical application, considering that the refund period, refund duty ratio, credit evaluation and price sensitivity of different business subclasses are large, the second price adding parameter can be determined according to the risk bearing capacity, and the lower the risk bearing capacity is, the higher the second price adding parameter is. In this embodiment, the second pricing parameter may be used as a pricing coefficient for the reactive risk bearing capacity. The lower the risk bearing capacity, the greater the corresponding price coefficient. Wherein the risk bearing capacity of the different services may be pre-configured, e.g. a higher second pricing parameter is set for conservative services, i.e. for services with a low risk bearing capacity.

Correspondingly, the price adding coefficient is obtained according to the product of the first price adding parameter and the second price adding parameter. That is, the addition coefficient=the first addition parameter×the second addition parameter, that is, the addition coefficient= (Σ predicted refund ratio of different refund periods x exchange rate fluctuation addition factor) ×the second addition parameter.

In this embodiment, the price adding coefficient is determined together based on the risk bearing capability, refund proportion of different refund periods and fluctuation of exchange rate, and various factors affecting the exchange rate damage risk are comprehensively considered.

In practical application, the service platform may have other exchange rate risk control policies besides the exchange rate risk control policy of the price adding coefficient, so that the influence of different exchange rate risk control policies on the exchange rate can be adjusted according to the expected duty ratio of the exchange rate risk control policies of the price adding coefficient in all exchange rate risk control policies. For example, if the desired ratio of the risk control policy for the price addition is equal to the desired ratio of the total exchange rate risk control policy, the second price addition parameter is increased, and if the desired ratio of the risk control policy for the price addition is equal to the desired ratio of the total exchange rate risk control policy, the second price addition parameter is decreased. For example, the service platform includes a policy a for risk control by using a price adding coefficient, and a risk hedging policy B, and if the duty ratio of the policy a needs to be increased, the second price adding parameter is increased. If the duty cycle of strategy B needs to be increased, the second pricing parameter is decreased.

In this embodiment, the price adding coefficient is determined together by the implementation condition of the exchange rate control risk policy, the refund proportion of different refund periods and the condition of exchange rate fluctuation, so that various factors affecting the exchange rate damage risk are comprehensively considered.

In another embodiment, the risk bearing capacity, implementation of the exchange rate control risk policy, refund proportion of different refund periods and exchange rate fluctuation are comprehensively considered to jointly determine the price adding coefficient, as shown in fig. 7, including:

Step 702, determining a first price adding parameter according to predicted refund proportion and exchange rate fluctuation price adding factors of a target transaction service in different refund periods; wherein, the predicted refund proportion and the exchange rate fluctuation price adding factor are positively correlated with the first price adding parameter.

Step 704, determining a second pricing parameter according to the risk bearing capacity, wherein the lower the risk bearing capacity is, the higher the second pricing parameter is.

In step 706, the third price adding parameter is determined according to the expected duty ratio of the risk control policy of the price adding coefficient in the all exchange rate risk control policy, and the larger the expected duty ratio of the risk control policy of the price adding coefficient in the all exchange rate risk control policy is, the larger the third price adding parameter is.

Step 708, obtaining the price adding coefficient according to the product of the first price adding parameter, the second price adding coefficient and the first price adding parameter.

Specifically, the following cases of actual traffic are considered in the present embodiment:

1. Because the market fluctuation rate of different currency pairs is large, different price adding strategies are required to be customized for the different currency pairs;

2. Because refund period, refund duty ratio, credit evaluation and price sensitivity of different business subclasses are large, different price adding strategies are required to be customized for the different business subclasses;

3. due to the continuous change of the foreign exchange market and the continuous development of each business subclass, model parameters and price adding strategies need to be updated regularly and support manual triggering update;

4. Since exchange rate fluctuations and exchange loss risk are strongly related to refund cycle, refund cycle characteristics of each business subclass & currency pair need to be applied as a dominant factor in the price-adding strategy.

Thus, factors affecting the price-adding coefficient in this embodiment include the predicted refund proportion of different refund cycles, the rate fluctuation price-adding factor, the risk bearing capacity, and the desired duty cycle of the price-adding coefficient's risk control strategy in the overall rate risk control strategy.

The predicted refund proportion of different refund periods is generated through prediction; because the exchange rate fluctuation and the refund-transaction time interval are strongly related, different prices need to be added to different interval periods, a model needs to be built, and the refund interval periods are sliced and proportionally predicted based on historical transaction records.

And the exchange rate fluctuation is added with a price factor and is generated through prediction. Different refund cycles correspond to different rates fluctuating risks, with rate fluctuating price factors being the price to cover this part of the risk.

First price parameter = predicted refund ratio of Σ different refund periods exchange rate fluctuation price factor. Specifically, when a business inquires, it cannot be judged which specific refund cycle the transaction will fall on, so the final price is obtained by weighting.

Risk bearing capacity for evaluating the sub-business original exchange rate refund risk; if the model evaluation is more conservative, the price adding amplitude can be managed by adjusting the business risk coefficient. Wherein the second pricing parameter is used to characterize the risk bearing capacity, the weaker the risk bearing capacity, the higher the second pricing parameter.

The larger the expected duty ratio of the risk control strategy of the price coefficient at the whole exchange rate risk control strategy, and the larger the second price parameter. The third price adding parameter is used for representing the expected duty ratio of the risk control strategy of the price adding coefficient in the whole exchange rate risk control strategy, and the larger the expected duty ratio of the risk control strategy of the price adding coefficient in the whole exchange rate risk control strategy is, the larger the third price adding parameter is.

Price coefficient= (predicted refund ratio of Σ different refund periods x exchange rate fluctuation price factor) x second price parameter x third price parameter.

The first price adding parameter may be determined based on a fixed prediction frequency, such as a first day of each month according to the prediction, and the price adding coefficient may be further determined according to the first price adding parameter, the second price adding parameter, and the third price adding parameter. And (3) storing a price coefficient table by using a joint primary key of (business, currency pair) and taking the price coefficient as a key value.

In practical application, as shown in fig. 8: the method comprises the following steps:

1. and receiving an original exchange rate refund exchange rate price inquiry request of the user for the currency pair AAABBB.

2. And judging whether the user has the original exchange rate refund service subscribed and in the production at present or not, if so, carrying out the original exchange rate refund price adding calculation, if not, skipping the step, and reporting the price according to the common flow.

3. And selecting pre-calculated price adding of different currency pairs of different businesses according to the business and currency pair information, and adjusting the price of the market exchange rate.

4. The original remittance rate after the processing is directly reported and returned to the merchant.

According to the method, the possible refund proportion of a user in different refund time intervals in the future is calculated based on the historical refund behaviors of the user, the fluctuation risk of the exchange rate possibly faced by a third party payment mechanism in the different time intervals is predicted through the fluctuation rate calculation of the historical fair exchange rate, the refund proportion and the exchange rate fluctuation rate obtained through calculation are converted into the exchange rate damage risk possibly brought by the original exchange rate refund service, and when the user is quoted, a corresponding price adding strategy is carried out to cover the related exchange loss risk brought by refund. In the business application process, through predicting the refund behaviors and the exchange rate fluctuation of the user, calculating the exchange rate damage risk brought by the refund behaviors possibly occurring, and controlling the exchange rate damage risk within a reasonable range through price adjustment.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a transaction service processing device for realizing the transaction service processing method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the transaction processing device or devices provided below may refer to the limitation of the transaction processing method hereinabove, and will not be described herein.

In one embodiment, as shown in fig. 9, there is provided a transaction service processing apparatus including:

the service request module 902 is configured to obtain a service processing request of a target transaction service pair for specifying a transaction currency pair.

The price adding processing module 904 is configured to query a price adding coefficient of a designated transaction currency pair of the target transaction service in response to the service processing request, where the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a rate fluctuation price adding factor of the designated transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor.

And the quotation module 906 is used for determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate.

The business processing module 908 is configured to execute the business processing request based on the exchange rate bid.

In another embodiment, the transaction service processing further comprises:

the refund proportion prediction module is used for acquiring target historical refund data and target historical transaction data which are related to the target transaction business in the maximum refund period; and predicting the predicted refund proportion of the target transaction business in different refund periods according to the target historical refund data and the target historical transaction data.

In another embodiment, the transaction service processing further comprises:

the exchange rate fluctuation measuring and calculating module is used for determining exchange rate fluctuation rates of the appointed transaction currency pairs in different periods according to the historical exchange rate data of the appointed transaction currency pairs in the maximum refund period; and determining the exchange rate fluctuation price adding factor according to the exchange rate fluctuation rates in the interval time in the period according to the designated trading currencies of different times.

In another embodiment, the exchange rate fluctuation measuring module is used for determining the exchange rate fluctuation price adding factor according to the exchange rate fluctuation rate of the designated transaction currency at different times and the time interval in different periods.

In another embodiment, the refund proportion prediction module is configured to determine a target object feature according to historical transaction data of the target transaction service; determining a target user group in a service platform according to the characteristics of the target object; the historical refund data of the target user group in the maximum refund period in the service platform is determined to be the target historical refund data, and the historical transaction data of the target user group in the maximum refund period in the service platform is determined to be the target historical transaction data.

In another embodiment, the refund proportion prediction module is configured to determine a plurality of different refund periods according to the maximum refund period; and taking the duty ratio of the target historical refund data in the target historical transaction data in each different refund period as the predicted refund proportion of the target transaction service refund period.

In another embodiment, the price adding processing module is configured to determine a first price adding parameter according to a predicted refund proportion and a fluctuating price adding factor of exchange rate of the target transaction service in different refund periods; wherein, the predicted refund proportion and the exchange rate fluctuation price adding factor are positively correlated with the first price adding parameter; and determining the price adding coefficient according to the first price adding parameter.

In another embodiment, the pricing processing module is configured to determine a second pricing parameter according to the risk bearing capacity, the lower the risk bearing capacity, the higher the second pricing parameter; or determining a second price adding parameter according to the expected duty ratio of the risk control strategy of the price adding coefficient in all exchange rate risk control strategies, wherein the larger the expected duty ratio of the risk control strategy of the price adding coefficient in all exchange rate risk control strategies is, the larger the second price adding parameter is, and obtaining the price adding coefficient according to the product of the first price adding parameter and the second price adding parameter.

In another embodiment, the pricing processing module is configured to determine a second pricing parameter according to the risk bearing capacity, the lower the risk bearing capacity, the higher the second pricing parameter; determining a third price adding parameter according to the expected duty ratio of the price adding coefficient risk control strategy in all exchange rate risk control strategies, wherein the larger the expected duty ratio of the price adding coefficient risk control strategy in all exchange rate risk control strategies is, the larger the third price adding parameter is; and obtaining the price adding coefficient according to the product of the first price adding parameter, the second price adding coefficient and the first price adding parameter.

The respective modules in the transaction service processing device may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing transaction data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a transaction service processing method.

It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor implementing the steps of the transaction processing method of each of the embodiments described above when the computer program is executed.

In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor, implements the steps of the transaction service processing method of the above embodiments.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the transaction service processing method of the embodiments described above.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims (10)

1. A transaction service processing method, the method comprising:

acquiring a service processing request of a target transaction service pair for specifying a transaction currency pair;

Inquiring a price adding coefficient of the appointed transaction currency pair of the target transaction service in response to the service processing request, wherein the price adding coefficient is calculated based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the appointed transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor;

Determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate;

and executing the business processing request based on the exchange rate quotation.

2. The method of claim 1, wherein determining the manner in which the predicted refund proportion of the target transaction traffic is within the supported maximum refund period comprises:

Acquiring target historical refund data and target historical transaction data related to the target transaction service in the maximum refund period;

And predicting the predicted refund proportion of the target transaction business in different refund periods according to the target historical refund data and the target historical transaction data.

3. The method of claim 1, wherein determining the manner in which the exchange rate fluctuation plus value factor for the specified transaction currency pair comprises:

determining exchange rate fluctuation rates of the specified transaction currency pairs at different times in different periods according to the historical exchange rate data of the specified transaction currency pairs in the maximum refund period;

And determining the exchange rate fluctuation price adding factor according to the exchange rate fluctuation rates of the designated transaction currencies at different times in the period interval time.

4. A method according to claim 3, wherein determining the rate fluctuation addition factor from the rate fluctuation rates of the specified transaction currencies at different times versus time intervals within different periods comprises:

And determining the exchange rate fluctuation price adding factor according to the exchange rate fluctuation rate of the designated transaction currency at different times and the time intervals in different periods.

5. The method of claim 2, wherein obtaining target historical refund data and target historical transaction data relating to the target transaction business during the maximum refund period comprises:

Determining target object characteristics according to the historical transaction data of the target transaction service;

determining a target user group in a service platform according to the target object characteristics;

And determining historical refund data of the target user group in the maximum refund period in the service platform as target historical refund data, and determining historical transaction data of the target user group in the maximum refund period in the service platform as target historical transaction data.

6. The method of claim 2, wherein predicting the predicted refund proportion of the target transaction traffic at different refund periods based on the target historical refund data and the target historical transaction data comprises:

Determining a plurality of different refund periods according to the maximum refund period;

And taking the duty ratio of the target historical refund data in each different refund period in the target historical transaction data as the predicted refund proportion of the refund period of the target transaction service.

7. A method according to any one of claims 2 to 3, wherein the manner in which the valence coefficients are determined comprises:

determining a first price adding parameter according to the predicted refund proportion of the target transaction business in different refund periods and the exchange rate fluctuation price adding factor; wherein the predicted refund proportion and the exchange rate fluctuation price adding factor are both positively correlated with the first price adding parameter;

And determining a price adding coefficient according to the first price adding parameter.

8. The method of claim 7, wherein the method further comprises:

Determining a second price adding parameter according to the risk bearing capacity, wherein the lower the risk bearing capacity is, the higher the second price adding parameter is; or determining a second price adding parameter according to the expected duty ratio of the price adding coefficient risk control strategy in all exchange rate risk control strategies, wherein the larger the expected duty ratio of the price adding coefficient risk control strategy in all exchange rate risk control strategies is, the larger the second price adding parameter is;

the determining the price adding coefficient according to the first price adding parameter comprises the following steps: and obtaining the price adding coefficient according to the product of the first price adding parameter and the second price adding parameter.

9. The method of claim 7, wherein the method further comprises:

determining a second price adding parameter according to the risk bearing capacity, wherein the lower the risk bearing capacity is, the higher the second price adding parameter is;

determining a third price adding parameter according to the expected duty ratio of the price adding coefficient risk control strategy in all exchange rate risk control strategies, wherein the larger the expected duty ratio of the price adding coefficient risk control strategy in all exchange rate risk control strategies is, the larger the third price adding parameter is;

The determining the price adding coefficient according to the first price adding parameter comprises the following steps: and obtaining the price adding coefficient according to the product of the first price adding parameter, the second price adding coefficient and the first price adding parameter.

10. A transaction service processing device, the device comprising:

The business request module is used for acquiring a business processing request of a target transaction business pair designated transaction currency pair;

The price adding processing module is used for responding to the service processing request and inquiring a price adding coefficient of the appointed transaction currency pair of the target transaction service, wherein the price adding coefficient is obtained by calculation based on at least one of a predicted refund proportion of the target transaction service in a supported maximum refund period and a exchange rate fluctuation price adding factor of the appointed transaction currency pair; the exchange rate fluctuation price adding factor and the predicted refund proportion are positively correlated with the price adding factor;

The quotation module is used for determining the exchange rate quotation of the designated transaction currency pair of the target transaction service according to the price adding coefficient and the current original exchange rate;

and the business processing module is used for executing the business processing request based on the exchange rate quotation.