CN116503177A - Financial business risk assessment method based on big data and big data wind control platform - Google Patents

Abstract

The invention discloses a financial business risk assessment method based on big data and a big data wind control platform, and relates to the field of financial wind control or other related technical fields, wherein the risk assessment method comprises the following steps: receiving a business risk assessment request sent by a business system; determining a risk assessment policy based on the service identification, wherein the risk assessment policy at least comprises: a risk assessment variable set; calling a source risk data set corresponding to the user side from the big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set; and sending the risk assessment index set to a decision center, and performing risk assessment on the target financial business by the decision center based on the risk assessment index set and a preset assessment rule. The invention solves the technical problems of large calculation workload and lower evaluation efficiency in the related technology in a financial business risk evaluation mode in a business system.

Description

Financial business risk assessment method based on big data and big data wind control platform

Technical Field

The invention relates to the field of financial wind control and other related technical fields, in particular to a financial business risk assessment method based on big data and a big data wind control platform.

Background

With the rapid expansion of financial services and the increasing growth of financial products, the purchase demands of users on financial products are increasing, especially in the field of credit loans, various financial institutions release various credit products, the development of the credit services makes competition stronger, the scenes of the credit services become more complex, and in order to reduce bad account rate when the financial institutions transact financial services such as credit loans, the financial institutions need to perform risk assessment.

In the related technology, the risk assessment of the financial business is carried out in a business system, the risk policy iteration needs a large amount of business system code development, the business system is required to carry out business handling, the risk assessment is also required, the workload is large, the assessment efficiency is low, a card list of the business system is easy to cause, the change of the risk cannot be rapidly dealt with, the business system cannot effectively introduce third party data, and the comprehensive and effective assessment cannot be carried out.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the invention provides a financial business risk assessment method and a big data wind control platform based on big data, which at least solve the technical problems of high calculation workload and low assessment efficiency in a financial business risk assessment mode in a business system in the related technology.

According to an aspect of the embodiment of the present invention, there is provided a financial business risk assessment method based on big data, including: receiving a business risk assessment request sent by a business system, wherein the business risk assessment request at least comprises: a business identifier of a target financial business and a user identifier of a user handling the target financial business; determining a risk assessment policy based on the service identifier, wherein the risk assessment policy at least comprises: a risk assessment variable set; calling a source risk data set corresponding to the user side from a big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set; and sending the risk assessment index set to a decision center, and carrying out risk assessment on the target financial business by the decision center based on the risk assessment index set and a preset assessment rule.

Optionally, before receiving the service risk assessment request sent by the service system, the method further includes: acquiring historical risk information and historical risk assessment results of each financial service, and extracting risk assessment variables based on the corresponding relation between the historical risk information and the historical risk assessment results to obtain a risk assessment variable set; and generating the risk assessment strategy based on the service identification of each financial service and the risk assessment variable set, and storing the risk assessment strategy into a risk data center.

Optionally, calling a source risk data set corresponding to the user side from a big data center based on the user identifier, and processing the source risk data set based on the risk assessment variable, so as to obtain a risk assessment index set, where the step of obtaining the risk assessment index set includes: determining a target time point for transacting the target financial business; invoking the source risk data set of the user side before the target time point from the big data center based on the target time point; and calculating the source risk data set based on the risk assessment variable set to obtain the risk assessment index set.

Optionally, the step of calculating the source risk data set based on the risk assessment variable set to obtain the risk assessment index set includes: traversing the source risk data in the source risk data set, and extracting target features corresponding to the risk evaluation variables under the condition that any risk evaluation variable in the risk evaluation variable set exists in the source risk data to obtain a target feature set; and calculating all the target features based on a preset calculation rule to obtain the risk assessment index set.

Optionally, after sending the risk assessment index set to a decision center, further comprising: receiving a risk assessment result sent by the decision center; and generating a business execution result and sending the business execution result to the business system under the condition that the risk assessment result indicates that the target financial business is at risk, and terminating the business system to transact the target financial business.

Optionally, the financial business risk assessment method based on big data further comprises: acquiring risk nodes of each financial service, wherein the risk nodes are nodes with risks in the process of transacting or executing the financial service; and configuring a risk assessment flow for each financial service based on the risk nodes.

According to another aspect of the embodiment of the present invention, there is also provided a big data wind control platform, including: the flow center is connected with a business system of a financial institution and receives a business risk assessment request sent by the business system; the large data center is in butt joint with a third party system and calls a source risk data set of the third party system; the risk data center determines a risk assessment strategy based on a target financial business identifier carried by the business risk assessment request, and processes the source risk data set corresponding to the user side handling the target financial business based on the risk assessment strategy to obtain a risk assessment index set; and the decision center receives the risk assessment index set sent by the risk data center, and carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule to obtain a risk assessment result.

Optionally, the big data wind control platform further includes: and the operation center performs visual display on the risk assessment result generated by the decision center.

Optionally, the big data wind control platform further includes: the risk data center is further configured to receive the risk assessment result sent by the decision center, generate a service execution result when the risk assessment result indicates that the target financial service has a risk, and send the service execution result to the service system.

According to another aspect of the embodiment of the present invention, there is also provided a financial business risk assessment device based on big data, including: the receiving unit is used for receiving a business risk assessment request sent by a business system, wherein the business risk assessment request at least comprises: a business identifier of a target financial business and a user identifier of a user handling the target financial business; a determining unit, configured to determine a risk assessment policy based on the service identifier, where the risk assessment policy at least includes: a risk assessment variable set; the calling unit is used for calling the source risk data set corresponding to the user side from the big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set; the sending unit is used for sending the risk assessment index set to a decision center, and the decision center carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule.

Optionally, the financial business risk assessment device based on big data further comprises: the first acquisition module is used for acquiring historical risk information and historical risk assessment results of each financial service, extracting risk assessment variables based on the corresponding relation between the historical risk information and the historical risk assessment results, and obtaining a risk assessment variable set; the first generation module is used for generating the risk assessment strategy based on the service identification of each financial service and the risk assessment variable set, and storing the risk assessment strategy to a risk data center.

Optionally, the calling unit includes: the first determining module is used for determining a target time point for transacting the target financial business; the first calling module is used for calling the source risk data set of the user side before the target time point from the big data center based on the target time point; and the first calculation module is used for calculating the source risk data set based on the risk assessment variable set to obtain the risk assessment index set.

Optionally, the first computing module includes: the first traversal sub-module is used for traversing the source risk data in the source risk data set, and extracting target features corresponding to the risk evaluation variables under the condition that any risk evaluation variable in the risk evaluation variable set exists in the source risk data to obtain a target feature set; the first calculation sub-module is used for calculating all the target features based on a preset calculation rule to obtain the risk assessment index set.

Optionally, the financial business risk assessment device based on big data further comprises: the first receiving module is used for receiving the risk assessment result sent by the decision center; and the second generation module is used for generating a service execution result and sending the service execution result to the service system when the risk assessment result indicates that the target financial service has risk, and the service system terminates the transaction of the target financial service.

Optionally, the financial business risk assessment device based on big data further comprises: the second acquisition module is used for acquiring risk nodes of each financial service, wherein the risk nodes are nodes with risks in the process of transacting or executing the financial service; and the first configuration module is used for configuring a risk assessment flow for each financial service based on the risk node.

According to another aspect of the embodiment of the present invention, there is further provided a computer readable storage medium, where the computer readable storage medium includes a stored computer program, and when the computer program runs, the device where the computer readable storage medium is controlled to execute any one of the financial business risk assessment methods based on big data.

According to another aspect of the embodiments of the present invention, there is also provided an electronic device including one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any of the above-mentioned big data based financial business risk assessment methods.

In the present disclosure, the method comprises the following steps: firstly, receiving a business risk assessment request sent by a business system, and then determining a risk assessment strategy based on a business identifier, wherein the risk assessment strategy at least comprises: and the risk assessment variable set is then called from the big data center based on the user identification, the source risk data set is processed based on the risk assessment variable to obtain a risk assessment index set, and finally the risk assessment index set is sent to the decision center, and the decision center carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule.

In the method, when risk assessment is carried out on the target financial business, the business system is only responsible for business handling, a risk assessment request is sent to the big data wind control platform, and after risk assessment is carried out by the big data wind control platform, an assessment result is returned to the business system, so that the assessment efficiency is improved, the condition that a card list is caused by large assessment calculation amount of the business system is avoided, and further the technical problems that in the related art, in a mode of carrying out financial business risk assessment in the business system, the calculation workload is large and the assessment efficiency is low are solved.

In the method, the big data center is in butt joint with the third-party data system, business risk assessment is carried out based on the big data, comprehensive risk data can be obtained, financial risk is comprehensively assessed, risk assessment accuracy is improved, and bad account risk is reduced.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:

FIG. 1 is a flow chart of an alternative big data based financial business risk assessment method in accordance with an embodiment of the present invention;

FIG. 2 is an alternative big data wind control platform architecture according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an alternative big data based financial transaction risk assessment device in accordance with an embodiment of the present invention;

fig. 4 is a block diagram of a hardware structure of an electronic device (or mobile device) of a financial transaction risk assessment method based on big data according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that, the financial business risk assessment method and the apparatus thereof based on big data in the present disclosure may be used in the financial wind control field when performing risk assessment on financial business based on big data, and may also be used in any field other than the financial wind control field when performing risk assessment on financial business based on big data.

It should be noted that, related information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present disclosure are information and data authorized by a user or sufficiently authorized by each party, and the collection, use and processing of related data need to comply with related laws and regulations and standards of related countries and regions, and be provided with corresponding operation entries for the user to select authorization or rejection. For example, an interface is provided between the system and the relevant user or institution, before acquiring the relevant information, the system needs to send an acquisition request to the user or institution through the interface, and acquire the relevant information after receiving the consent information fed back by the user or institution.

The following embodiments of the present invention are applicable to various systems/applications/devices for risk assessment of financial transactions based on big data. According to the invention, the business system is in butt joint with the big data wind control platform, the business system sends a risk assessment request to the big data wind control platform, the big data wind control platform calculates mass financial data, extracts risk assessment indexes, carries out risk assessment on target financial businesses to be transacted based on the risk assessment indexes, and sends assessment results to the business system, and the big data wind control platform carries out unified and effective management on a risk assessment flow and an assessment strategy, so that the risk assessment efficiency is improved, and the change of financial risks is rapidly dealt with.

The present invention will be described in detail with reference to the following examples.

Example 1

According to an embodiment of the present invention, there is provided an embodiment of a big data based financial transaction risk assessment method, it being noted that the steps shown in the flowchart of the figures may be performed in a computer system such as a set of computer executable instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from that herein.

FIG. 1 is a flow chart of an alternative big data based financial business risk assessment method according to an embodiment of the present invention, as shown in FIG. 1, comprising the steps of:

step S101, receiving a service risk assessment request sent by a service system, where the service risk assessment request at least includes: business identification of the target financial business, user identification of the user handling the target financial business;

step S102, determining a risk assessment strategy based on the service identification, wherein the risk assessment strategy at least comprises: a risk assessment variable set;

step S103, calling a source risk data set corresponding to the user terminal from the big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set;

Step S104, the risk assessment index set is sent to a decision center, and the decision center carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule.

Through the steps, firstly, a business risk assessment request sent by a business system is received, and then, a risk assessment strategy is determined based on a business identifier, wherein the risk assessment strategy at least comprises: and the risk assessment variable set is then called from the big data center based on the user identification, the source risk data set is processed based on the risk assessment variable to obtain a risk assessment index set, and finally the risk assessment index set is sent to the decision center, and the decision center carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule.

In this embodiment, when performing risk assessment on a target financial service, the service system is only responsible for handling the service, and sends a risk assessment request to the big data wind control platform, and after the big data wind control platform performs risk assessment, the assessment result is returned to the service system, thereby improving the assessment efficiency, avoiding the situation that the service system has a card bill due to large assessment calculation amount, and further solving the technical problems of large calculation workload and low assessment efficiency in the related art in the manner of performing financial service risk assessment in the service system.

Embodiments of the present invention will be described in detail with reference to the following steps.

It should be noted that, the implementation main body of the embodiment of the present invention is a big data wind control platform, the big data wind control platform is in butt joint with a service system, a plurality of service scenarios are predefined in the service system, after a financial service event is generated in the service scenario, the service system generates a service risk assessment request according to a service identifier of a target financial service to be transacted and a user identifier of a user transacting the target financial service, the request is sent to the big data wind control platform, the big data wind control platform processes a policy formulation, extracts a risk assessment index, returns an assessment result to the service system after performing operations such as risk assessment, and the service system performs pass or reject operations on the target financial service according to the assessment result.

Optionally, before receiving the service risk assessment request sent by the service system, the method further includes: acquiring historical risk information and historical risk assessment results of each financial service, and extracting risk assessment variables based on the corresponding relation between the historical risk information and the historical risk assessment results to obtain a risk assessment variable set; and generating a risk assessment strategy based on the service identification and the risk assessment variable set of each financial service, and storing the risk assessment strategy into a risk data center.

It should be noted that, in the embodiment of the present invention, before risk assessment is performed, a risk assessment indicator is used as a criterion of risk assessment of a financial service, a risk assessment variable capable of being used as an assessment criterion is required to be determined, a financial service of a financial service is generated by a financial institution in a historical time period, that is, a historical risk assessment result is counted, then, based on the historical risk assessment result, historical risk information of a historical target financial service generating risk is obtained, risk characteristics are extracted from the historical risk information and used as a risk assessment variable, for example, the historical tax times of a user are used as one of the risk assessment variables, or the historical effective trust times of the user at the financial institution are used as one of the risk assessment variables, then, a risk assessment policy of the financial service is generated according to the risk assessment variable, a service identifier and the risk assessment policy of the financial service are stored in a mapping relationship to a risk data center, and when the risk assessment is required to be performed on a certain financial service, the risk data center is only required to be obtained, and the risk assessment policy corresponding to the financial service is called.

Optionally, the financial business risk assessment method based on big data further comprises: acquiring risk nodes of each financial service, wherein the risk nodes are nodes with risks in the process of transacting or executing the financial service; and configuring a risk assessment flow for each financial business based on the risk nodes.

It should be noted that, there are multiple business scenarios in the business system, such as pre-loan approval of loan business, management in loan of loan business, post-loan operation of loan business, etc., and the risk nodes of each financial business in different business scenarios are different, so in order to evaluate the risk of each financial business more accurately, it is necessary to configure a risk evaluation flow for each financial business based on different risk nodes, and meanwhile, a self-triggering program may be set for each financial business.

Step S101, a business risk assessment request sent by a business system is received.

It should be noted that, in the embodiment of the present invention, the big data wind control platform is connected to the service system, and the user (including but not limited to, a person, an enterprise/company) handles the financial service through the user side (mobile phone, iPad, PC, etc.) or the service client of the financial institution, when the handled financial service needs to perform risk assessment, the user side or the service client automatically generates a service risk assessment request and sends the service risk assessment request to the big data wind control platform, where the service risk assessment request carries a service identifier of the target financial service and a user identifier corresponding to the user side handling the target financial service, the service identifier is used for matching the risk assessment policy, and the user identifier is used for invoking financial risk data related to the user.

Step S102, determining a risk assessment strategy based on the service identification, wherein the risk assessment strategy at least comprises: a set of risk assessment variables.

It should be noted that, after receiving the business risk assessment request, the business identifier is extracted, and a corresponding risk assessment policy is called from the risk data center according to the business identifier, where risk assessment variables for assessing the risk of the target financial business are stored.

Step S103, calling a source risk data set corresponding to the user side from the big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set.

Optionally, step S103 includes: determining a target time point for transacting target financial business; invoking a source risk data set of the user side before the target time point from the big data center based on the target time point; and calculating the source risk data set based on the risk assessment variable set to obtain a risk assessment index set.

It should be noted that, a big data center is deployed in the big data wind control platform, and the big data center is in docking with a third party system, where the third party system includes but is not limited to: the third party tax system and the third party credit investigation system can directly call source risk data of target users from a plurality of third party systems based on the call interfaces, in order to enable a risk assessment result to be more accurate, a target time point of handling target financial services by a user side needs to be acquired, all source risk data before the time point is acquired, and then the acquired source risk data are matched with risk assessment variables to obtain a risk assessment index set.

Alternatively, when the user first transacts the financial business, a user portrait can be established for the user, the user portrait is matched with the user identifier, the key risk information of the user can be intuitively obtained through the user portrait, and the user portrait is updated in real time along with the behavior data of the user.

It should be noted that, in the embodiment of the present invention, source risk data may also be obtained through a user portrait created by a user, the user portrait may be docked with real-time behavior data of the user, key risk features are extracted according to real-time data generated by the user, and stored and visually displayed, when the user handles loan service, risk feature data of the user may be obtained by viewing the user portrait through a user identifier, and then the obtained risk feature data is matched with risk assessment variables, so as to obtain a risk assessment index set.

Optionally, the step of calculating the source risk data set based on the risk assessment variable set to obtain the risk assessment index set includes: traversing the source risk data in the source risk data set, and extracting target features corresponding to the risk evaluation variables under the condition that any risk evaluation variable in the risk evaluation variable set exists in the source risk data to obtain a target feature set; and calculating all target features based on a preset calculation rule to obtain a risk assessment index set.

It should be noted that, after the source risk data set of the target user is obtained, all source risk data in the set are traversed, when any risk assessment variable in the risk assessment variable set is found in the source risk data, variable data corresponding to the risk assessment variable, that is, target features are extracted, and then all features are calculated to obtain a risk assessment index set, for example, after tax data of the target user in the past year is obtained, the risk assessment variable can be obtained according to: the tax amount is collected, data related to the tax amount is extracted, and then the tax amount of which the tax amount is higher than the average tax amount is calculated based on a preset calculation rule, for example, and is marked as a normal tax, and the normal tax is used as a risk assessment index.

Step S104, the risk assessment index set is sent to a decision center, and the decision center carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule.

Optionally, after sending the risk assessment index set to the decision center, further comprising: receiving a risk assessment result sent by a decision center; and under the condition that the risk assessment result indicates that the target financial business has risk, generating a business execution result, sending the business execution result to a business system, and terminating the business system to transact the target financial business.

It should be noted that, the big data wind control platform is further configured with a decision center, after the decision center receives the risk assessment index set, the decision center may perform risk assessment according to a preset assessment rule, for example, by counting the total number of the risk assessment index set, comparing the obtained target number with a preset number threshold, when the target number is greater than or equal to the preset number threshold, determining that the target financial service has risk, sending the risk assessment result to the service system, and rejecting the target financial service by the service system.

Alternatively, before the evaluation index set is acquired, a weight value is configured for each acquired source risk data, the weight value of each risk evaluation index is calculated according to the weight value of the source risk data, when the risk evaluation is performed, the weight value of each risk evaluation index in the risk evaluation index set is accumulated and calculated to obtain the risk value of the target financial service, when the risk value exceeds a preset risk threshold, an early warning prompt is sent to the service system, and the service system terminates the transaction of the target financial service.

The following describes in detail another embodiment.

Example two

In this embodiment, a big data wind control platform is provided, where the big data wind control platform corresponds to each implementation step in the first embodiment.

FIG. 2 is an alternative big data wind control platform frame composition according to an embodiment of the present invention, as shown in FIG. 2, the big data wind control platform comprises: the method comprises the following steps of a flow center, a big data center, a risk data center, a decision center and an operation center, wherein each module specifically executes the following functions and the interaction process among the modules:

it should be noted that, in the embodiment of the present invention, before risk assessment is performed, a risk assessment indicator is used as a criterion of risk assessment of a financial service, a risk assessment variable capable of being used as an assessment criterion is required to be determined, a financial service of a financial service is generated by a financial institution in a historical time period, that is, a historical risk assessment result is counted, then, based on the historical risk assessment result, historical risk information of a historical target financial service generating risk is obtained, risk characteristics are extracted from the historical risk information and used as a risk assessment variable, for example, the historical tax times of a user are used as one of the risk assessment variables, or the historical effective trust times of the user at the financial institution are used as one of the risk assessment variables, then, a risk assessment policy of the financial service is generated according to the risk assessment variable, a service identifier and the risk assessment policy of the financial service are stored in a mapping relationship to a risk data center, and when the risk assessment is required to be performed on a certain financial service, the risk data center is only required to be obtained, and the risk assessment policy corresponding to the financial service is called.

It should be noted that, there are multiple business scenarios in the business system, such as pre-loan approval of loan business, management in loan of loan business, post-loan operation of loan business, etc., and the risk nodes of each financial business in different business scenarios are different, so in order to evaluate the risk of each financial business more accurately, it is necessary to configure a risk evaluation flow for each financial business based on different risk nodes, and meanwhile, a self-triggering program may be set for each financial business.

The flow center is connected with a business system of the financial institution and receives a business risk assessment request sent by the business system;

it should be noted that in the embodiment of the present invention, the flow center is connected to the service system, and the user (including, but not limited to, a person, an enterprise/company) handles the financial service through the user side (mobile phone, iPad, PC, etc.) or the service client of the financial institution, when the handled financial service needs to perform risk assessment, the user side or the service client automatically generates a service risk assessment request and sends the service risk assessment request to the flow center, where the service risk assessment request carries a service identifier of the target financial service and a user identifier corresponding to the user side handling the target financial service, the service identifier is used for matching the risk assessment policy, and the user identifier is used for invoking the financial risk data related to the user.

The large data center is in butt joint with the third party system and calls a source risk data set of the third party system;

it should be noted that, a big data center is deployed in the big data wind control platform, and the big data center is in docking with a third party system, for example, a third party tax system, a third party credit investigation system, and the like, and the big data center can directly call source risk data of the target user from a plurality of third party systems based on a call interface.

The risk data center determines a risk assessment strategy based on a target financial business identifier carried by the business risk assessment request, and processes a source risk data set corresponding to a user side handling the target financial business based on the risk assessment strategy to obtain a risk assessment index set;

after receiving the business risk assessment request, extracting a business identifier from the business risk assessment request, and calling a corresponding risk assessment strategy from a risk data center according to the business identifier, wherein a risk assessment variable for assessing the risk of a target financial business is stored in the risk assessment strategy;

the risk data center in the big data wind control platform is also used for receiving the risk assessment result sent by the decision center, generating a service execution result under the condition that the risk assessment result indicates that the target financial service has risk, and sending the service execution result to the service system.

When performing risk assessment, in order to make the risk assessment result more accurate, a target time point of a user side handling a target financial service needs to be acquired, all source risk data of a target user before the time point is acquired from a big data center, and then the acquired source risk data is matched with a risk assessment variable to obtain a risk assessment index set.

It should be noted that, after the source risk data set of the target user is obtained, all source risk data in the set are traversed, when any risk assessment variable in the risk assessment variable set is found in the source risk data, variable data corresponding to the risk assessment variable, that is, target features are extracted, and then all features are calculated to obtain a risk assessment index set, for example, after tax data of more than one past year is obtained, the risk assessment variable can be obtained according to: the tax amount extracts data related to the tax amount, and then calculates a tax amount of tax amount higher than the average tax amount based on a preset calculation rule, for example, and marks the tax amount as a normal tax amount, and counts the normal tax amount as a risk assessment index.

The decision center receives the risk assessment index set sent by the risk data center, and carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule to obtain a risk assessment result;

It should be noted that, the big data wind control platform is further configured with a decision center, after the decision center receives the risk assessment index set, the decision center can perform risk assessment according to a preset assessment rule, for example, by counting the total number of the risk assessment index set, comparing the obtained target number with a preset number threshold, when the target number is greater than or equal to the preset number threshold, determining that the target financial service has risk, sending the risk assessment result to the service system, and rejecting the service system to pass through the target financial service.

Big data wind control platform still includes: and the operation center performs visual display on the risk assessment result generated by the decision center.

The operation center can visually display the risk assessment result generated by the decision center, visually display the risk assessment index of the user obtained through calculation in a chart form, and update the user portrait corresponding to the target user according to the extracted data key features.

The following detailed description is directed to alternative embodiments.

As shown in fig. 2, a flow center, a big data center, a risk data center, a decision center and an operation center are deployed in the big data wind control platform, wherein the flow center is in butt joint with a service system, the service system comprises a plurality of service scenes (shown as a service scene 1, a service scene 2, a service scene 3 and the like in fig. 2), the service system sends a service risk assessment request to the flow center, the flow center deploys a risk assessment flow, source risk data is firstly requested to the big data center, the big data center is in butt joint with a third party system (shown as a three-party credit system, a three-party tax system and the like in fig. 2), data of the third party system is called, the big data center stores and updates the called data in real time, then the risk data center calculates a risk assessment index, the risk data center sends the calculated risk assessment index to the decision center, the decision center returns an assessment result to the service system according to a preset assessment rule after the risk assessment, and the decision center carries out visual display on the assessment result obtained by the decision center.

Specifically, the interaction flow of each module in the big data wind control platform is as follows:

step one, a feeding trigger event: risk packages are generated from business scenarios (e.g., financial businesses that may be at risk, such as loan business requests, etc.), packages that include loan approval events, or post-loan management events in loans, etc.

Step two, an event starting flow: and the big data wind control platform receives the risk feed, the flow center accesses the feed event, and a risk event processing flow is formulated according to the risk nodes defined by the flow engine.

Step three, constructing risk assessment indexes: the risk data center determines a risk assessment strategy based on a risk component, then traverses source risk data according to a risk assessment variable in the risk assessment strategy, extracts a risk assessment index from the source risk data, and the process of acquiring the risk assessment index comprises data preparation and logic calculation.

The process of constructing the risk assessment index comprises (1) data preparation of variables, and flexibly scheduling source risk data by a large data platform according to source risk data required, wherein the scheduling data comprises a plurality of data contents including but not limited to: three-party credit data access, three-party tax data access and in-line customer behavior data access; (2) And (3) logically calculating, namely extracting feature data according to the risk evaluation variable traversal source risk data, calculating according to a preset calculation rule to obtain a risk evaluation index, and realizing the processing of the user risk data derivative variable.

Step four, risk assessment decision: the risk data center transmits the risk assessment index to the decision center through the scheduling flow, and the decision center carries out risk decision judgment according to the defined strategy rules and anti-fraud rules.

Step five, decision visual operation: and the big data wind control platform transmits the risk entry information generated in the flow scheduling and the risk assessment index generated in the risk data center to the operation center, and the operation center performs visual display and related operation management.

Step six, returning a response result: and the big data wind control platform returns the final decision result generated by the policy center to a service system for calling the big data risk platform, and the service system continues to execute the service flow according to the decision result.

In this embodiment, the business system interfaces with the big data wind control platform, the business system sends a risk assessment request to the big data wind control platform, the big data wind control platform calculates massive financial data, extracts risk assessment indexes, carries out risk assessment on target financial businesses to be transacted based on the risk assessment indexes, and sends assessment results to the business system, and the big data wind control platform can flexibly configure risk assessment strategies to intensively define and manage risk assessment flows, so that the risk assessment efficiency is improved, and changes of risks are rapidly handled.

The following describes in detail another embodiment.

Example III

The financial business risk assessment device based on big data provided in this embodiment includes a plurality of implementation units, each implementation unit corresponding to each implementation step in the above-mentioned embodiment.

FIG. 3 is a schematic diagram of an alternative big data based financial transaction risk assessment device, as shown in FIG. 3, according to an embodiment of the present invention, which may include: a receiving unit 31, a determining unit 32, a calling unit 33, a transmitting unit 34, wherein,

the receiving unit 31 is configured to receive a service risk assessment request sent by a service system, where the service risk assessment request at least includes: business identification of the target financial business, user identification of the user handling the target financial business;

a determining unit 32, configured to determine a risk assessment policy based on the service identifier, where the risk assessment policy at least includes: a risk assessment variable set;

the calling unit 33 is configured to call a source risk data set corresponding to the user side from the big data center based on the user identifier, and process the source risk data set based on the risk assessment variable to obtain a risk assessment index set;

The sending unit 34 is configured to send the risk assessment index set to a decision center, and the decision center performs risk assessment on the target financial service based on the risk assessment index set and a preset assessment rule.

The risk assessment device receives, through the receiving unit 31, a service risk assessment request sent by a service system, where the service risk assessment request includes at least: business identification of the target financial business, user identification of the user handling the target financial business; determining, by the determining unit 32, a risk assessment policy based on the service identification, wherein the risk assessment policy comprises at least: a risk assessment variable set; calling a source risk data set corresponding to the user side from the big data center based on the user identification through a calling unit 33, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set; the risk assessment index set is sent to the decision center through the sending unit 34, and the decision center performs risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule.

In this embodiment, when performing risk assessment on a target financial service, the service system is only responsible for handling the service, and sends a risk assessment request to the big data wind control platform, and after the big data wind control platform performs risk assessment, the assessment result is returned to the service system, thereby improving the assessment efficiency, avoiding the situation that the service system has a card bill due to large assessment calculation amount, and further solving the technical problems of large calculation workload and low assessment efficiency in the related art in the manner of performing financial service risk assessment in the service system.

Optionally, the financial business risk assessment device based on big data further comprises: the first acquisition module is used for acquiring historical risk information and historical risk assessment results of each financial service, extracting risk assessment variables based on the corresponding relation between the historical risk information and the historical risk assessment results, and obtaining a risk assessment variable set; the first generation module is used for generating a risk assessment strategy based on the service identification and the risk assessment variable set of each financial service, and storing the risk assessment strategy into the risk data center.

Optionally, the calling unit includes: the first determining module is used for determining a target time point for transacting the target financial business; the first calling module is used for calling a source risk data set of the user side before the target time point from the big data center based on the target time point; the first calculation module is used for calculating the source risk data set based on the risk assessment variable set to obtain a risk assessment index set.

Optionally, the first computing module includes: the first traversal sub-module is used for traversing the source risk data in the source risk data set, and extracting target features corresponding to the risk evaluation variables under the condition that any risk evaluation variable in the risk evaluation variable set exists in the source risk data to obtain a target feature set; the first computing sub-module is used for computing all target features based on a preset computing rule to obtain a risk assessment index set.

Optionally, the financial business risk assessment device based on big data further comprises: the first receiving module is used for receiving the risk assessment result sent by the decision center; and the second generation module is used for generating a business execution result and sending the business execution result to the business system when the risk assessment result indicates that the target financial business has risk, and the business system terminates the transaction of the target financial business.

Optionally, the financial business risk assessment device based on big data further comprises: the second acquisition module is used for acquiring risk nodes of each financial service, wherein the risk nodes are nodes with risks in the process of transacting or executing the financial service; the first configuration module is used for configuring a risk assessment flow for each financial business based on the risk nodes.

The risk assessment apparatus may further include a processor and a memory, wherein the receiving unit 31, the determining unit 32, the calling unit 33, the transmitting unit 34, and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.

The processor includes a kernel, and the kernel fetches a corresponding program unit from the memory. The kernel can be provided with one or more than one, and risk assessment is carried out on the target financial business by adjusting kernel parameters.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), which includes at least one memory chip.

According to another aspect of the embodiment of the present invention, there is also provided a computer readable storage medium, where the computer readable storage medium includes a stored computer program, and when the computer program is executed, the device on which the computer readable storage medium is located is controlled to execute any one of the financial business risk assessment methods based on big data.

According to another aspect of the embodiments of the present invention, there is also provided an electronic device including one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any of the above-described big data based financial business risk assessment methods.

The present application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: receiving a business risk assessment request sent by a business system, wherein the business risk assessment request at least comprises: business identification of the target financial business, user identification of the user handling the target financial business; determining a risk assessment policy based on the service identification, wherein the risk assessment policy at least comprises: a risk assessment variable set; calling a source risk data set corresponding to the user side from the big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set; and sending the risk assessment index set to a decision center, and performing risk assessment on the target financial business by the decision center based on the risk assessment index set and a preset assessment rule.

Fig. 4 is a block diagram of a hardware structure of an electronic device (or mobile device) of a financial transaction risk assessment method based on big data according to an embodiment of the present invention. As shown in fig. 4, the electronic device may include one or more (shown in fig. 4 as 402a, 402b, … …,402 n) processors 402 (the processors 402 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, etc. processing means), a memory 404 for storing data. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a keyboard, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 4 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the electronic device may also include more or fewer components than shown in FIG. 4, or have a different configuration than shown in FIG. 4.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (12)

1. A financial business risk assessment method based on big data, comprising:

receiving a business risk assessment request sent by a business system, wherein the business risk assessment request at least comprises: a business identifier of a target financial business and a user identifier of a user handling the target financial business;

determining a risk assessment policy based on the service identifier, wherein the risk assessment policy at least comprises:

a risk assessment variable set;

calling a source risk data set corresponding to the user side from a big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set;

and sending the risk assessment index set to a decision center, and carrying out risk assessment on the target financial business by the decision center based on the risk assessment index set and a preset assessment rule.

2. The risk assessment method according to claim 1, further comprising, prior to receiving a business risk assessment request sent by a business system:

acquiring historical risk information and historical risk assessment results of each financial service, and extracting risk assessment variables based on the corresponding relation between the historical risk information and the historical risk assessment results to obtain a risk assessment variable set;

And generating the risk assessment strategy based on the service identification of each financial service and the risk assessment variable set, and storing the risk assessment strategy into a risk data center.

3. The risk assessment method according to claim 1, wherein the steps of calling the source risk data set corresponding to the user side from a big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set include:

determining a target time point for transacting the target financial business;

invoking the source risk data set of the user side before the target time point from the big data center based on the target time point;

and calculating the source risk data set based on the risk assessment variable set to obtain the risk assessment index set.

4. A risk assessment method according to claim 3, wherein the step of calculating the source risk data set based on the risk assessment variable set to obtain the risk assessment index set comprises:

traversing the source risk data in the source risk data set, and extracting target features corresponding to the risk evaluation variables under the condition that any risk evaluation variable in the risk evaluation variable set exists in the source risk data to obtain a target feature set;

And calculating all the target features based on a preset calculation rule to obtain the risk assessment index set.

5. The risk assessment method according to claim 1, further comprising, after sending the risk assessment index set to a decision center:

receiving a risk assessment result sent by the decision center;

and generating a business execution result and sending the business execution result to the business system under the condition that the risk assessment result indicates that the target financial business is at risk, and terminating the business system to transact the target financial business.

6. The risk assessment method according to claim 1, further comprising:

acquiring risk nodes of each financial service, wherein the risk nodes are nodes with risks in the process of transacting or executing the financial service;

and configuring a risk assessment flow for each financial service based on the risk nodes.

7. A big data wind control platform, comprising:

the flow center is connected with a business system of a financial institution and receives a business risk assessment request sent by the business system;

the large data center is in butt joint with a third party system and calls a source risk data set of the third party system;

The risk data center determines a risk assessment strategy based on a target financial business identifier carried by the business risk assessment request, and processes the source risk data set corresponding to the user side handling the target financial business based on the risk assessment strategy to obtain a risk assessment index set;

and the decision center receives the risk assessment index set sent by the risk data center, and carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule to obtain a risk assessment result.

8. The big data wind control platform of claim 7, further comprising:

and the operation center performs visual display on the risk assessment result generated by the decision center.

9. The big data wind platform of claim 7, wherein the risk data center is further configured to receive the risk assessment result sent by the decision center, generate a service execution result if the risk assessment result indicates that the target financial service is at risk, and send the service execution result to the service system.

10. A financial business risk assessment device based on big data, comprising:

The receiving unit is used for receiving a business risk assessment request sent by a business system, wherein the business risk assessment request at least comprises: a business identifier of a target financial business and a user identifier of a user handling the target financial business;

a determining unit, configured to determine a risk assessment policy based on the service identifier, where the risk assessment policy at least includes: a risk assessment variable set;

the calling unit is used for calling the source risk data set corresponding to the user side from the big data center based on the user identification, and processing the source risk data set based on the risk assessment variable to obtain a risk assessment index set;

the sending unit is used for sending the risk assessment index set to a decision center, and the decision center carries out risk assessment on the target financial business based on the risk assessment index set and a preset assessment rule.

11. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program when run controls a device in which the computer readable storage medium is located to perform the big data based financial business risk assessment method according to any of claims 1 to 6.

12. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the big data based financial business risk assessment method of any of claims 1 to 6.