CN114140245A

CN114140245A - Organization risk assessment method, device and equipment and computer storage medium

Info

Publication number: CN114140245A
Application number: CN202111486834.4A
Authority: CN
Inventors: 冯于羚
Original assignee: China Construction Bank Corp
Current assignee: China Construction Bank Corp
Priority date: 2021-12-07
Filing date: 2021-12-07
Publication date: 2022-03-04

Abstract

The embodiment of the application discloses a mechanism risk assessment method, a device, equipment and a computer storage medium, which can acquire historical risk data corresponding to a risk data item and historical control data corresponding to an anti-risk measure data item of a mechanism to be assessed; determining a first risk factor corresponding to a risk data item according to historical risk data; determining a second risk factor corresponding to the counter risk measure data item according to the historical control data; and according to a preset data fusion rule, fusing the first prediction result and the second prediction result to obtain a risk evaluation result of the mechanism to be evaluated, wherein the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor. The risk assessment result of the mechanism to be assessed is comprehensively obtained through the risk condition of the mechanism to be assessed and the control measure of the risk, and assessment accuracy is high.

Description

Organization risk assessment method, device and equipment and computer storage medium

Technical Field

The present application relates to the field of financial risk assessment technologies, and in particular, to a method, an apparatus, a device, and a computer storage medium for assessing risk of an organization.

Background

In order to strengthen the supervision of the financial industry and resist the threat of unstable factors (such as financial transaction abnormity of the financial institutions in the regions) to the financial industry, the ability to master the financial risk coping capability of the financial institutions in the industry becomes important.

Knowledge of the financial institution's ability to respond to financial risk may be determined by performing a risk assessment on the financial institution. However, most of the current risk assessment methods for financial institutions are realized through single data calculation, and have certain limitations, and financial risks existing in related institutions cannot be accurately pre-assessed, so that supervision is poor.

Disclosure of Invention

The embodiment of the application provides a mechanism risk assessment method, a mechanism risk assessment device and a computer storage medium, and can improve the accuracy of mechanism risk assessment.

In one aspect, an embodiment of the present application provides an organization risk assessment method, including:

acquiring historical risk data of a risk data item corresponding to an organization to be evaluated and historical control data of an anti-risk measure data item corresponding to the organization to be evaluated;

determining a first risk factor corresponding to a risk data item according to historical risk data;

determining a second risk factor corresponding to the counter risk measure data item according to the historical control data;

and according to a preset data fusion rule, fusing the first prediction result and the second prediction result to obtain a risk evaluation result of the mechanism to be evaluated, wherein the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor.

In some embodiments, the risk data items include first risk data items, and the historical risk data includes first historical data corresponding to the first risk data items; the first risk factor comprises a first event risk factor;

determining a first risk factor corresponding to a risk data item according to historical risk data, wherein the first risk factor comprises the following steps:

and determining a first event risk factor corresponding to the first risk data item according to the first historical data.

In some embodiments, the counter-risk measure data items include a first counter-risk measure data item, and the historical control data includes second historical data corresponding to the first counter-risk measure data item; the second risk factor comprises a first event anti-risk factor;

determining a second risk factor corresponding to the counter-risk measure data item according to the historical control data, wherein the second risk factor comprises the following steps:

and determining a first event anti-risk factor corresponding to the first anti-risk measure data item according to the second historical data.

In some embodiments, the first predicted outcome comprises a first predictor and the second predicted outcome comprises a second predictor;

before a first prediction result and a second prediction result are fused according to a preset data fusion rule to obtain a risk evaluation result of a structure to be evaluated, the method comprises the following steps:

according to a preset weighting rule, carrying out weighted calculation on the first event risk factor to obtain a first predictor result; and

and according to a preset weighting rule, carrying out weighting calculation on the first event anti-risk factor to obtain the second predictor result.

In some embodiments, the first risk data item includes one or more of regional and business scale information, first customer information, business information and first channel information corresponding to the organization to be assessed;

the first anti-risk measure data item comprises control measure information corresponding to an organization to be evaluated, and the control measure information comprises control measure information for one or more of regions, operation scales, customer groups, product businesses and channels.

In some embodiments, the risk data items include second risk data items, and the historical risk data includes third historical data corresponding to the second risk data items; the first risk factor comprises a second event risk factor;

and determining a second event risk factor corresponding to the second risk data item according to the third history data.

In some embodiments, the counter-risk measure data items include second counter-risk measure data items, and the historical control data includes fourth historical data corresponding to the second counter-risk measure data items; the second risk factor comprises a second event risk factor;

determining a second risk factor corresponding to the counter risk measure risk item according to the historical control data, wherein the second risk factor comprises the following steps:

and determining a second event anti-risk factor corresponding to the second anti-risk measure data item according to the fourth historical data.

In some embodiments, the first predictor comprises a third predictor and the second predictor comprises a fourth predictor;

performing weighted calculation on the second event risk factor according to a preset weighting rule to obtain a third predictor result; and

and performing weighted calculation on the second event anti-risk factor according to a preset weighting rule to obtain a fourth predictor result.

On the other hand, the embodiment of the present application provides an organization risk assessment device, and the device includes:

the acquisition module is used for acquiring historical risk data corresponding to the risk data items and historical control data corresponding to the counter-risk measure data items of the mechanism to be evaluated;

the first determining module is used for determining a first risk factor corresponding to the risk data item according to the historical risk data;

the second determining module is used for determining a second risk factor corresponding to the counter risk measure data item according to the historical control data;

and the fusion module is used for fusing the first prediction result and the second prediction result according to a preset data fusion rule to obtain a risk evaluation result of the mechanism to be evaluated, wherein the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor.

In another aspect, an embodiment of the present application provides an organization risk assessment apparatus, including: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements an institutional risk assessment method as described in an aspect.

In another aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method for risk assessment of an organization according to an aspect is implemented.

In yet another aspect, the present application provides a computer program product, and when executed by a processor of an electronic device, the instructions of the computer program product cause the electronic device to perform the organization risk assessment method according to an aspect.

The method, the device, the equipment and the computer storage medium for evaluating the risk of the organization can acquire historical risk data corresponding to risk data items of the organization to be evaluated and historical control data corresponding to counter-risk measure data items; determining a first risk factor corresponding to a risk data item according to historical risk data; determining a second risk factor corresponding to the counter risk measure data item according to the historical control data; and according to a preset data fusion rule, fusing the first prediction result and the second prediction result to obtain a risk evaluation result of the mechanism to be evaluated, wherein the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor. The risk assessment result of the mechanism to be assessed is comprehensively obtained through the risk condition of the mechanism to be assessed and the control measure of the risk, and assessment accuracy is high.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic flow chart diagram of an organization risk assessment method provided in one embodiment of the present application;

FIG. 2 is a schematic flow chart of an organization risk assessment method in another embodiment of the present application;

FIG. 3 is a schematic flow chart of an organization risk assessment method in yet another embodiment of the present application;

FIG. 4 is a schematic structural diagram of an organization risk assessment device according to another embodiment of the present application;

FIG. 5 is a schematic hardware configuration diagram of an organization risk assessment device according to another embodiment of the present application;

fig. 6 is a schematic hardware structure diagram of an organization risk assessment device according to an embodiment of the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

In order to solve the prior art problems, embodiments of the present application provide a mechanism risk assessment method, apparatus, device, and computer storage medium. The following first introduces an organization risk assessment method provided in the embodiment of the present application. And it should be understood that the technical solution of the present application, such as obtaining, storing, using, processing, etc., of data all conform to relevant regulations of national laws and regulations.

Fig. 1 shows a schematic flow chart of an organization risk assessment method according to an embodiment of the present application. As shown in fig. 1, the method may include steps S101 to S104:

s101, acquiring historical risk data of a risk data item corresponding to an organization to be evaluated and historical control data of an anti-risk measure data item corresponding to the organization to be evaluated.

S102, determining a first risk factor corresponding to a risk data item according to historical risk data;

s103, determining a second risk factor corresponding to the counter risk measure data item according to the historical control data;

and S104, according to a preset data fusion rule, fusing the first prediction result and the second prediction result to obtain a risk evaluation result of the mechanism to be evaluated, wherein the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor.

According to the mechanism risk assessment method, historical risk data of a risk data item corresponding to a mechanism to be assessed and historical control data of an anti-risk measure data item corresponding to the mechanism to be assessed can be obtained; determining a first risk factor corresponding to a risk data item according to historical risk data; determining a second risk factor corresponding to the counter risk measure data item according to the historical control data; and according to a preset data fusion rule, fusing the first prediction result and the second prediction result to obtain a risk evaluation result of the mechanism to be evaluated, wherein the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor. The risk assessment result of the mechanism to be assessed is comprehensively obtained through the risk condition of the mechanism to be assessed and the control measure of the risk, and assessment accuracy is high.

In order to better fulfill the supervision requirement and strengthen the anti-risk supervision capability of the financial institution facing the main financial events, such as the compliance management of the group anti-money laundering and financial sanctions, optionally, in the embodiment of the present application, the main financial event risks facing the institution to be evaluated and the control capability of the risks, such as the aspects of evaluating money laundering risks and sanction risks and the control measures of the risks, can be integrated. The method comprises the steps of obtaining corresponding data item information for calculation, objectively evaluating inherent risks faced by anti-risk work and effectiveness of control measures, quantitatively evaluating residual risks, generating a comprehensive risk evaluation report of an evaluation organization on a plurality of financial events (at least two events), and obtaining a more comprehensive and accurate risk evaluation result.

Therefore, in the embodiment of the application, in order to realize the accuracy of risk assessment, the anti-money laundering risk capacity of the organization to be assessed can be comprehensively evaluated by two aspects of the first event risk (such as money laundering risk) and the first event anti-risk measure (money laundering risk control measure); and comprehensively measuring the sanction compliance management capability of the organization from two aspects of the risk of the second event (such as sanction risk) and the counter-risk measure (such as sanction risk control measure) of the second event of the organization to be evaluated. Therefore, optionally, in the embodiment of the present application, taking the first event as a money laundering event and the second event as a sanction event as an example, the anti-money laundering risk capability and sanction compliance management capability of the organization to be evaluated may be calculated through steps S101 to S104, respectively.

Optionally, in the process of evaluating the anti-money laundering capability of the institution to be evaluated, the risk data item in step S101 includes a first risk data item. For example, the first risk data item may include a plurality of items of data information as money laundering risk indicators. In one example, the first risk data item may include one or more of regional and business scale information, customer information, business information, and channel information corresponding to the organization to be assessed.

Correspondingly, the anti-risk data item may correspondingly include a first anti-risk measure data item. For example, the first anti-risk measure data item as the anti-money laundering control measure index may include anti-money laundering control measure information corresponding to the institution to be evaluated, where the control measure information includes control measure information for one or more of the region and business scale, customer group, product business, and channel.

The historical risk data acquired at step S101 may include first historical data corresponding to the first risk data item, and the historical control data may include second historical data corresponding to the first anti-risk measure data item, wherein:

the first historical data may be data information of a historical annual record of the institution under evaluation corresponding to the first risk data item. The first historical data of the first risk data item can be used for determining a risk factor corresponding to the first risk data item, namely a first event risk factor included in the first risk factor;

the second historical data may be data information of the corresponding first counter-risk measure data item of the historical annual record of the organization to be assessed. The second historical data of the first anti-risk measure data item can be used for determining a risk factor corresponding to the first anti-risk measure data item, namely a first event anti-risk factor included in the second risk factor.

In an embodiment of the present application, the first predictor may include a first predictor, and the first predictor may be determined by a first event risk factor as a preliminary first event risk (hereinafter also referred to as "money laundering risk") predictor. The second predictor may include a second predictor result, which may be determined by the first event anti-risk factor, as a preliminary first event anti-risk (hereinafter also referred to as "anti-money laundering control") predictor result. And performing fusion calculation on the first predictor result and the second predictor result to obtain a risk evaluation result of the mechanism to be evaluated on the anti-money laundering capacity. According to the embodiment of the application, the comprehensive and comprehensive analysis is carried out through the corresponding multiple data information in the money laundering risk and the money laundering prevention control measure, and objective and accurate evaluation results can be obtained.

In order to obtain objective and accurate money laundering risk assessment results and meet requirements of related laws, regulations and normative documents, optionally, a comprehensive first risk data item is established in the embodiment of the application, so that corresponding historical data can be obtained for assessment and calculation.

For example, the first risk data item may include one or more of regional and business scale information, first customer information, business information, and first channel information corresponding to the organization to be assessed, where:

a. the region and business scale information may include the following data information:

a1. the situation of money laundering, suspicious financing and upstream crime in the region (registration place or operation place) of the organization to be evaluated, whether the organization is adjacent to foreign countries and regions where money laundering, suspicious financing or upstream crime and illegal activities are active or whether the organization belongs to high-risk countries and regions;

a2. the region of the organization to be evaluated receives information such as the number of clients, transaction amount, asset scale and the like related to criminal inquiry, freezing, deduction, supervision agency, public security agency inquiry, freezing, deduction and the like of the judicial institution;

a3. the information of the number of reports, the number of customers, the transaction amount and the like of general suspicious transactions and key suspicious transactions related to the region where the organization to be evaluated is located, which are reported by the organization to be evaluated; and

a4. the number of the network points of the organization to be evaluated in the area, the number of customers, the size of the customer assets, the transaction amount, the market share level and the like.

b. The first customer information may include the following data information:

b1. information such as the number of customers, the asset scale, the transaction amount and the corresponding proportion of the organization to be evaluated;

the method comprises the following steps that information such as the quantity and proportion of criminal inquiry, freezing, deduction and related bank investigation related to a client of an organization to be evaluated is obtained;

b2. the client identity information integrity degree and the abundance degree of the organization to be evaluated and the information of the understanding degree of the transaction background and the purpose of the client;

b3. the organization to be evaluated identifies the distribution of the client identities in different modes, such as identity verification on the spot, identity verification by adopting a reliable technical means, identity identification ratio identification by a third-party organization and other information;

b4. the stock right or control right structure of the unnatural customer of the organization to be evaluated has the condition information of the risk of the same controller;

b4. the client of the organization to be evaluated is from the condition of the high risk country or region, and the like.

c. The service information may include the following data information:

c1. product business scale information of the organization to be evaluated, such as account number information, total amount of managed assets information, annual transaction amount and the like;

c2. the product business information of the organization to be evaluated belongs to the known money laundering cases and money laundering type techniques;

c3. the product business of the organization to be evaluated faces to the main customer group, and the number of high-risk customers and the corresponding asset scale, transaction amount and proportion information;

c4. product business records of an organization to be evaluated track information such as fund sources, the destination degree, the association degree with cash, the cash transaction amount and proportion and the like; whether product business applies information such as new technologies that may affect customer due diligence and tracking of funds transactions.

d. The first channel information may include the following data information:

d1. the risk degree information of the channel coverage range (offline network point quantity and distribution area, online accessible region range) and corresponding regions (including overseas countries and regions) of the organization to be evaluated;

d2. the method comprises the steps that an organization to be evaluated establishes customer number and risk level distribution information of a business relation through a corresponding channel;

d3. and the number of clients handling the services, the transaction number and the amount of money, the main types of the handled services, the risk level and other information of the institution to be evaluated through the corresponding channel.

The information of each data item in the region and operation scale information, the first customer information, the business information and the first channel information included in the first risk data item can be used as money laundering risk indexes to participate in the comprehensive money laundering risk evaluation of the organization to be evaluated.

It should be understood that the method of the embodiments of the present application may be performed based on the first server. In this embodiment of the present application, each item of data information included in the first risk data item may be stored in a database of one or more corresponding servers (e.g., a related financial management department server, a bank operation server, etc.), history data of all the first risk data items of the organization to be evaluated may be stored in the database, and the history data of the first risk data item is stored in association with an organization number of the corresponding organization to be evaluated. In the evaluation process, the first server can call the corresponding data information from the related storage database according to the organization number of the organization to be evaluated.

Or, the various pieces of data information may also be recorded in a data storage area (such as a local storage area or a cloud storage area) of the first server in a manual entry manner.

In this embodiment of the application, the historical risk data obtained in step S101 may include first historical data corresponding to the first risk data item, that is, historical data of the first risk data item in a historical year, for example, historical data corresponding to the first customer information item and the first channel information item in 2011. In the embodiment of the application, the first risk data item is determined as an inherent risk data item, and the money laundering risk index is predicted on the basis of the evaluation of the historical data of the first risk data item in the historical years.

Optionally, as shown in fig. 2, after the first historical data corresponding to the first risk data item is obtained in step S101, the corresponding risk factor may be determined in step S102, in this embodiment of the application, the first risk factor includes a first event risk factor, and step S102 specifically includes step S1021:

and S1021, determining a first event risk factor corresponding to the first risk data item according to the first historical data.

Exemplarily, a risk factor calculation rule is established in advance, and the acquired first historical data is processed through the risk factor calculation rule to obtain risk factors corresponding to various data information in the first risk data item.

For example, according to the acquired historical data of 2020, it can be known that, in the business information items of the organization to be evaluated, the total number of customers in the customer group to which the product a is oriented is B, and the number of high-risk customers (for example, individual customers engaged in high-risk profession or unit customers belonging to high-risk industry) in the customer group is C. And enabling the C/B to obtain the risk factors corresponding to the data item of the high-risk customer number in the service information item according to a pre-established risk factor calculation rule, wherein if the result of the C/B is 3%, the risk factor corresponding to the data item of the high-risk customer number is 0.03.

It should be understood that, in the embodiment of the present application, the risk factor calculation rule may be adjusted according to actual requirements, and is not limited herein.

After the first event risk factor corresponding to the first risk data item is determined through step S1021, it can be used to predict money laundering risk. Optionally, in this embodiment of the application, the first predicted data result may include a first predicted sub-result, and after step S1021, the method may further include step S105:

and S105, carrying out weighted calculation on the first event risk factor according to a preset weighting rule to obtain a first predictor result.

The first risk data item comprises a plurality of items of data item information, each item of data item information is used as index information, and a risk factor can be obtained based on the corresponding first historical data. The risk factor is used as a weighting coefficient of the corresponding data item information, and can be weighted with other data item information according to a preset weighting rule to obtain a corresponding first predictor result.

For example, if the risk factor of "the number of high-risk customers" in the business information data item is 0.03, and the risk factor of "the risk degree of the channel coverage" in the first channel information data item is 0.05, the risk factors of the two data items are weighted and calculated through a preset weighting rule, so that the money laundering risk total score is 0.08, and the risk total score is a first predictor.

It should be understood that, in the plurality of data item information included in the first risk data item, the risk factor corresponding to each data item may be weighted according to a preset weighting rule, and the total money laundering risk obtained is divided into the first predictor.

Optionally, in the embodiment of the present application, money laundering risk levels are divided according to the evaluation rules. And after the first predictor result is obtained, classifying the first predictor result into a corresponding money laundering risk grade.

Illustratively, the money laundering risk levels may be divided into five categories, i.e., high, medium, low, and low, according to the assessment plan, and the money laundering risk levels may be divided by scores, such as a high risk level of 5, a high risk level of 4, a medium risk level of 3, a low risk level of 2, and a low risk level of 1.

Or, for example, the plan may be divided into five categories of money laundering risk levels, namely, high, medium, low, and low according to the assessment, and the value range may be: a high risk rating of [1,0.8), a higher risk rating of [0.8,0.6), a medium risk rating of [0.6,0.4), a lower risk rating of [0.4,0.2), and a low risk rating of [0.2,0 ].

In which rank region the value of the first predictor falls, the corresponding money laundering risk rank is categorized. The higher the money laundering risk level of the first prediction outcome, the greater the money laundering risk faced.

In order to quantify the remaining money laundering risk of the institution to be evaluated, optionally, in the embodiment of the present application, the effectiveness of the anti-money laundering measure of the institution also needs to be evaluated. Thus, in step S101, the counter-risk measure data item includes a first counter-risk measure data item. For example, the first anti-risk measure data item may include control measure information corresponding to the organization to be assessed, where the control measure information includes control measure information items for one or more of the region and business scale, the customer group, the product business and the channel, and the first anti-risk measure data item may specifically include:

a. the money laundering risk management policy making condition of the organization to be evaluated and the matching degree of the policy and the identified risk, such as whether the organization expands the business range, including the region range, the business range, the customer range and the channel range, considers the corresponding money laundering risk or not, and makes a trial decision through a board party, a high-level management layer or a proper level;

b. whether the matching degree of the anti-money laundering internal control system of the organization to be evaluated and the supervision requirement is updated in time, the business operation rules of each line and the conditions of the embedded money laundering risk management measures in the system;

c. the anti-money laundering management department to be evaluated communicates with the business department, the customer management department, the channel department and each branch institution through mechanisms and information exchange conditions;

d. the method comprises the steps that the coverage, timeliness and quality of the work of customer due diligence investigation and customer risk grade division and adjustment of an organization to be evaluated, the integrity, accuracy and timeliness of customer identity data acquisition, storage and updating, the rationality of customer risk grade division indexes (including the condition of considering regions, product services and channel risks), and a mechanism for strengthening due diligence investigation and other control measures are adopted for customers with higher risks;

e. monitoring, analyzing and reporting mechanism and process rationality of large-amount and suspicious transactions of an organization to be evaluated, monitoring and analyzing system functions and acquiring information, monitoring and analyzing indexes and model design rationality and revision timeliness, and monitoring and analyzing conditions considering region, client, product service and channel risk;

f. the transaction record storage integrity and the query and retrieval convenience are realized;

g. the system has the advantages of soundness of a list screening working mechanism, comprehensiveness of covering business and client ranges, and functions of system early warning and backtracking screening.

It should be understood that the historical data information corresponding to the first counter-risk measure data item may be stored in a database of one or more servers, and the first server may retrieve the corresponding data information according to the organization number of the organization to be assessed.

Or, each item of data information corresponding to the first counter-risk measure data item may be recorded in the data storage area of the first server in a manual entry manner.

In this embodiment of the application, in step S101, the historical control data obtained in step S101 may include second historical data corresponding to the first counter-risk measure data item, that is, historical data of the first counter-risk measure data item in a historical year, such as historical data of an information item of "degree of matching between the internal control system of the anti-money laundering of the organization to be evaluated and the regulatory requirement" in 2011. In the embodiment of the application, the anti-money laundering risk index is predicted on the basis of the historical data of the first anti-risk measure data item in the historical years.

Optionally, after the second history data corresponding to the first anti-risk measure data item is obtained in step S101, the corresponding risk factor may be determined in step S103, in this embodiment of the present application, the second risk factor includes a first event anti-risk factor, and step S103 specifically includes step S1031:

and S1031, determining a first event anti-risk factor corresponding to the first anti-risk data item according to the second historical data.

Exemplarily, the acquired second historical data is processed through a risk factor calculation rule through a pre-established risk factor calculation rule, so as to obtain risk factors corresponding to various data information in the first anti-risk measure data item.

For example, according to the acquired historical data of 2020, it can be known that the matching degree of the anti-money laundering internal control system of the organization to be evaluated and the supervision requirement is D, then according to the pre-established risk factor calculation rule, let D · 2%, and then obtain the risk factor corresponding to the data item of "matching degree of the anti-money laundering internal control system of the organization to be evaluated and the supervision requirement", if D is 100%, the risk factor corresponding to the data item is 0.02.

After the first event anti-risk factor corresponding to the first anti-risk measure data item is determined through step S1031, it may be used to predict the anti-money laundering risk. Optionally, in this embodiment of the application, the second predicted data result may include a second sub-result, and after step S1031, the method may further include step S106:

s106, carrying out weighted calculation on the anti-risk factor of the first event according to a preset weighting rule to obtain a second predictor result.

The first counter-risk measure data item comprises a plurality of items of data item information, each item of data item information is used as index information, a risk factor can be obtained based on corresponding second historical data, the risk factor is used as a weight coefficient of corresponding data item information, and weighting calculation can be carried out on the risk factor and other data item information according to a preset weighting rule to obtain a corresponding second predictor result.

For example, if the risk factor of the "matching degree of the internal control system of the anti-money laundering of the organization to be evaluated with the supervision requirements" is 0.02, and the risk factor of the assignment of the communication mechanism and the information exchange condition between the anti-money laundering management department to be evaluated and the business department, the customer management department, the channel department and each branch organization "is 0.05, the risk factors of the two data items are weighted and calculated by the preset weighting rule, so that the money laundering risk total score is 0.07, and the risk total score is a second predictor result.

It should be understood that, in the plurality of data item information included in the first anti-risk measure data item, the risk factor corresponding to each data item may be weighted according to a preset weighting rule, and the total money laundering risk obtained is divided into the second predictor.

Optionally, in the embodiment of the present application, the anti-money laundering control level is divided according to the evaluation rule. And classifying the second predictor result into a corresponding anti-money laundering control level after obtaining the second predictor result. Illustratively, the evaluation plan may be divided into five types of anti-money laundering control levels, i.e., high, medium, low, and the anti-money laundering control levels may be divided by scores, such as a high control level of 5, a high control level of 4, a medium control level of 3, a low control level of 2, and a low control level of 1. Determining the effective degrees of the control measures according to the control levels in turn as follows: robust, satisfactory, general, inadequate, and significant drawbacks.

The rank region in which the score of the second predictor falls is classified into the corresponding control rank. The higher the control level, the more effective the corresponding anti-money laundering control measure is.

After the first predictor result and the second predictor result are obtained, the two predictor results can be subjected to fusion calculation in step S104, so that the evaluation result of the institution to be evaluated in the aspect of anti-money laundering risk capability is determined. Optionally, in this embodiment of the application, the step S104 may fuse the first prediction result and the second prediction result according to a preset data fusion rule to obtain a risk assessment result of the to-be-assessed mechanism, and specifically include the step S1041:

and S1041, fusing the first predictor result and the second predictor result according to a preset data fusion rule to obtain an anti-money laundering risk evaluation result of the to-be-evaluated organization.

The data fusion rule is used for performing fusion calculation on the first predictor result and the second predictor result so as to fully measure the money laundering risk of the organization to be evaluated and the comprehensive score of the effectiveness of the anti-money laundering control measure. The anti-money laundering risk assessment results obtained after fusion calculation can also be divided into 5 grades according to the assessment rules, wherein the grades are A (low risk), B (low risk), C (medium risk), D (high risk) and E (high risk). The risk grade of the anti-money laundering risk assessment result is determined after the first predictor result and the second predictor result are matched and combined.

Illustratively, the fusion calculation of the above two predictors can be performed by the data fusion rule of table 1 below.

TABLE 1

In order to objectively and accurately evaluate the remaining sanction compliance risk of the organization to be evaluated, in the embodiment of the present application, optionally, data information of a second event risk (hereinafter also referred to as a sanction risk) and a second event counter risk (hereinafter also referred to as a sanction control) is extracted and processed to obtain a corresponding sanction compliance evaluation result. Illustratively, the risk data items in step S101 include a second risk data item. For example, the second risk data item may include a plurality of items of data information as the sanction risk indicator. In one example, the second risk data item may include: and the second channel information, the second customer information and the region information of the organization to be evaluated.

Correspondingly, the anti-risk data item may correspondingly include a second anti-risk measure data item. Illustratively, the second anti-risk measure data item may be used as a sanction control measure validity index, and may include sanction control measure information corresponding to the institution to be evaluated, where the sanction control measure information may include information about organization architecture and personnel configuration information of the institution to be evaluated, customer money laundering risk assessment information, financial sanction system construction, internal communication and information transfer degree, customer identity identification flow and completion degree, record preservation conditions, sanction list screening and monitoring measures, information confidentiality measures, guarantee propulsion mechanism, and the like.

The historical risk data acquired at step S101 may include third historical data corresponding to the second risk data item, and the historical control data may include fourth historical data corresponding to the second counter-risk measure data item, wherein:

the third history data may be data information of a corresponding second risk data item of the historical annual record of the organization to be evaluated. The third history data of the second risk data item can be used for determining a risk factor corresponding to the second risk data item, that is, a second event risk factor included in the first risk factor;

the fourth historical data may be data information of the corresponding second counter-risk measure data item of the historical annual record of the organization to be assessed. The fourth historical data of the second counter risk measure data item can be used for determining a risk factor corresponding to the second counter risk measure data item, that is, a second event counter risk factor included in the second risk factor.

In this embodiment of the application, the first prediction result may include a third predictor result, and the third predictor result may be determined by the second event risk factor as a preliminary sanction risk prediction result. The second predictor may include a fourth predictor result, which may be determined by the second event anti-risk factor, as a preliminary sanction control effectiveness predictor. And performing fusion calculation on the third predictor result and the fourth predictor result to obtain a risk evaluation result of the mechanism to be evaluated on the sanction compliance management capability. According to the embodiment of the application, the comprehensive and comprehensive analysis is carried out through the corresponding multiple data information from the sanction risk and sanction control measures, and objective and accurate evaluation results can be obtained.

In order to objectively and accurately sanction risk assessment results and meet requirements of relevant laws, regulations and normative documents, optionally, a comprehensive second risk data item is established in the embodiment of the application, so that corresponding historical data can be obtained for assessment calculation.

For example, the second risk data item may include one or more of second channel information, second customer information, and regional information, wherein:

a. the second channel information may include the following data information:

a1. the organization to be evaluated uses a non-face-to-face channel in the evaluation period, and the number of clients transacting account opening or one-time financial services accounts for the proportion of the total number of the clients newly opening an account or transacting one-time financial services in the evaluation period;

a2. the institution to be evaluated uses foreign currency to settle accounts and settle accounts;

b. the second customer information may include the following data information:

the number of customers engaged in high-risk occupations; the number of customers of the country controlled by the new nationality, the registered place or the main business place for financial sanctioning in the evaluation period; evaluating cross-border transaction information in the total transaction number of the clients managed by the main body returning; evaluating the number of clients listed in the sanction list related to the clients managed by the subject.

And all the data item information in the second channel information, the second customer information and the region information can be used as sanction risk indexes to participate in sanction compliance risk comprehensive evaluation of the organization to be evaluated.

It should be understood that the method of the embodiments of the present application may be performed based on the first server. In this embodiment of the application, the data information included in the second risk data item may be stored in one or more corresponding server databases, respectively, and the historical data of the second risk data item is stored in association with the organization number of the corresponding organization to be assessed. In the evaluation process, the first server can call the corresponding data information from the related storage database according to the organization number of the organization to be evaluated.

In this embodiment of the application, the historical risk data acquired in step S101 may include third historical data corresponding to the second risk data item, that is, historical data of the second risk data item in a historical year, for example, historical data corresponding to the second customer information item in 2020. In the embodiment of the application, the second risk data item is determined to be an inherent risk data item, and the sanction compliance risk index is predicted based on the historical data of the second risk data item in the historical years.

Optionally, as shown in fig. 3, after the third history data corresponding to the second risk data item is obtained in step S101, the corresponding risk factor may be determined in step S102, in this embodiment, the first risk factor includes a second event risk factor, and step S102 specifically includes step S1022:

and S1022, determining a second event risk factor corresponding to the second risk data item according to the third history data.

Exemplarily, the acquired third history data can be processed through a risk factor calculation rule established in advance to obtain risk factors corresponding to each item of data information in the second risk data item.

For example, according to the acquired third history data of 2020, it can be known that in the second channel information item of the institution to be evaluated, the non-face-to-face channel is used in the evaluation period of the institution to be evaluated, and the proportion of the number of clients transacting account opening or one-time financial services to the total number of clients newly opening an account or transacting one-time financial services in the evaluation period is E. According to the pre-established risk factor calculation rule, the risk factor corresponding to the data item of the second channel information is E · 5%, and if E is 0.8, the risk factor corresponding to the data item of the second channel information is 0.04.

After determining the second event risk factor corresponding to the second risk data item through step S1022, the second event risk factor may be used to predict the sanction compliance risk. Optionally, in this embodiment of the application, the first predicted data result may include a third predictor result, and after step S1022, the method may further include step S107:

and S107, performing weighted calculation on the second event risk factor according to a preset weighting rule to obtain a third predictor result.

The second risk data item comprises a plurality of items of data item information, each item of data item information is used as index information, and a risk factor can be obtained based on the corresponding third history data. The risk factor is used as a weighting coefficient of the corresponding data item information, and can be weighted with other data item information according to a preset weighting rule to obtain a corresponding third predictor result.

For example, if the risk factor of the second channel information data item is 0.04 and the risk factor in the second customer information data item is 0.12, the risk factors of the two data items are weighted and calculated through a preset weighting rule, so that the money laundering risk total score is 0.16, and the risk total score is a third predictor.

It should be understood that, in the plurality of data item information included in the second risk data item, the risk factor corresponding to each data item may be weighted according to a preset weighting rule, and the total sanctioned risk obtained is divided into the third predictor.

Optionally, in the embodiment of the present application, the sanction risk level is divided according to the evaluation rule. And classifying the third predictor result into a corresponding sanction risk grade after the third predictor result is obtained.

Illustratively, the plan according to the evaluation may be divided into five types of sanction risk grades, i.e., high, medium, low and low, and the sanction risk grades may be divided by scores, such as a high risk grade of 5, a high risk grade of 4, a medium risk grade of 3, a low risk grade of 2 and a low risk grade of 1.

In which grade region the value of the third predictor falls, the corresponding sanction risk grade is classified. The higher the sanction risk level of the third prediction result, the greater the sanction compliance risk.

In order to quantify the remaining sanction risk of the organization to be evaluated, optionally, in the embodiment of the present application, the effectiveness of sanction control measures of the organization needs to be evaluated. Thus, in step S101, the counter-risk measure data item includes a second counter-risk measure data item. For example, the second anti-risk measure data item may include sanction compliance control measure information corresponding to the organization to be evaluated, and the sanction compliance control measure information may include organization architecture and personnel configuration information of the organization to be evaluated, money laundering risk evaluation information of the customer, financial sanction system construction, internal communication and information transfer degree, customer identification flow and completion degree, record preservation condition, sanction list screening and monitoring measures, information privacy measures, guarantee propulsion mechanism, and other information.

It should be understood that the historical data information corresponding to the second counter-risk measure data item may be stored in a database of one or more servers, and the first server may retrieve the corresponding data information according to the organization number of the organization to be assessed.

Or, each item of data information corresponding to the second counter-risk measure data item may be recorded in the data storage area of the first server in a manual entry manner.

In this embodiment, in step S101, the historical control data obtained in step S101 may include fourth historical data corresponding to the second counter-risk measure data item, that is, historical data of the second counter-risk measure data item in a historical year, such as historical data of the "internal communication and information transmission degree" information item in 2020. In the embodiment of the application, the sanction control risk index is predicted on the basis of the historical data of the second counter-risk measure data item in the historical years.

Optionally, after the fourth historical data corresponding to the second counter-risk measure data item is obtained in step S101, the corresponding risk factor may be determined in step S103, in this embodiment of the present application, the second risk factor includes a second event counter-risk factor, and step S103 specifically includes step S1032:

s1032, according to the fourth historical data, determining a second event anti-risk factor corresponding to the second anti-risk measure data item.

Exemplarily, the acquired fourth historical data is processed through a risk factor calculation rule through a pre-established risk factor calculation rule, so as to obtain risk factors corresponding to various data information in the second counter risk measure data item.

For example, according to the acquired historical data of 2020, if the customer identification process and the completion degree are F, F · 20% is calculated according to the pre-established risk factor calculation rule, and then the risk factor corresponding to the data item of "customer identification process and completion degree" is obtained, if F is 80%, the risk factor corresponding to the data item is 0.16.

After the second event counter-risk factor corresponding to the second counter-risk measure data item is determined through step S1032, it may be used to predict the sanction control effectiveness. Optionally, in this embodiment of the application, the second predicted data result may include a fourth sub-result, and after step S1032, the method may further include step S108:

and S108, carrying out weighted calculation on the second event anti-risk factor according to a preset weighting rule to obtain a fourth predictor result.

The second counter-risk measure data item comprises a plurality of items of data item information, each item of data item information is used as index information, a risk factor can be obtained based on corresponding fourth historical data, the risk factor is used as a weight coefficient of corresponding data item information, and weighting calculation can be carried out on the risk factor and other data item information according to a preset weighting rule to obtain a corresponding fourth predictor result.

For example, if the risk factor of the "customer identification process and completion degree" is 0.16, and the risk factor of the "screening and monitoring measure on the sanctioning list" is 2, the risk factors of the two data items are weighted and calculated through a preset weighting rule, so that the sanctioning control effectiveness risk is 2.16, and the risk is the fourth predictor.

It should be understood that, in the plurality of data item information included in the second counter-risk measure data item, the risk factor corresponding to each data item may be weighted according to a preset weighting rule, and the obtained sanction control effectiveness risk is always divided into the fourth predictor.

Optionally, in the embodiment of the present application, the sanction control level is divided according to the evaluation rule. And after the fourth predictor result is obtained, classifying the fourth predictor result into a corresponding sanction control level. For example, the plan may be divided into five categories, i.e., high, medium, low, and low, according to the evaluation, and the sanction control levels may be divided by scores, such as 5 for the high control level, 4 for the high control level, 3 for the medium control level, 2 for the low control level, and 1 for the low control level. Determining the effective degrees of the control measures according to the control levels in turn as follows: robust, satisfactory, general, inadequate, and significant drawbacks.

And classifying the corresponding control level according to the grade region of the score of the fourth predictor result. The higher the control level, the more effective the corresponding sanctioned control measure.

After the third predictor result and the fourth predictor result are obtained, the two predictor results can be subjected to fusion calculation in step S104, so that the evaluation result of the mechanism to be evaluated in terms of sanctioning compliance risk capability is determined. Optionally, in this embodiment of the application, the step S104 may fuse the first prediction result and the second prediction result according to a preset data fusion rule to obtain a risk assessment result of the to-be-assessed mechanism, and specifically include the step S1042:

and S1042, according to a preset data fusion rule, fusing the third predictor result and the fourth predictor result to obtain a sanction compliance risk assessment result of the mechanism to be assessed.

The data fusion rule can also be used for performing fusion calculation on the third predictor result and the fourth predictor result so as to fully measure the sanction risk of the mechanism to be evaluated and the comprehensive score of the effectiveness of sanction control measures. Illustratively, the fusion calculation of the above two predictors can be performed by the data fusion rule of table 2 below.

TABLE 2

Optionally, after obtaining the anti-money laundering risk assessment result and the sanction compliance risk assessment result, the method may further include:

s109, calling a risk assessment report template according to the anti-money laundering risk assessment result and the sanction compliance risk assessment result to generate a corresponding risk assessment report; and

and S110, storing and outputting a risk assessment report.

The institution to be assessed (i.e., legal financial institution) may establish or continuously adjust, refine, and pay attention to the execution of the control measures based on the risk assessment reports.

Optionally, in the embodiment of the present application, the association relationship between the risk assessment report and the enhanced risk management measure data may be preset. Therefore, after the risk assessment report of the mechanism to be assessed is obtained, when the high risk or higher risk condition reflected by the risk assessment report is determined or the effectiveness of the original control measure is insufficient, the corresponding enhanced risk management measure data can be automatically fed back according to the preset incidence relation, and the mechanism to be assessed is guided to carry out management adjustment.

For example, the enhanced risk management measure data may include measure data for:

a. according to the self-evaluation conclusion of money laundering risks, determining resource allocation and priority required by money laundering work, and warping strategy if necessary to ensure to be adaptive to risk management;

b. according to weak links of control measures found by evaluation, internal control system construction and work flow optimization are enhanced, a work mechanism is perfected, and internal inspection and audit are strict;

c. carrying out priority processing aiming at the high-risk client types found by evaluation, adopting strict client admission policies or strengthening due diligence investigation, improving the frequency of updating the information of the high-risk client, or strengthening the transaction monitoring and limiting of the high-risk client;

d. taking a reinforced control measure aiming at the high-risk service type found by evaluation, and setting limits on service admission, transaction frequency, transaction amount and the like;

e. adjusting and optimizing transaction monitoring indexes and list monitoring, and performing more frequent and deep examination on high-risk business activities found by evaluation;

f. carrying out risk prompt aiming at the problems found by evaluation;

g. strengthening the function construction of an information system and supporting the requirement of money laundering risk management;

h. other measures that can effectively control the risk.

According to the method, the anti-money laundering risk assessment result of the mechanism to be assessed can be determined according to the money laundering risk and the anti-money laundering control risk, and the sanction compliance risk assessment structure of the mechanism to be assessed can be determined according to the sanction risk and the sanction control measure risk, so that the financial supervision capacity of the mechanism to be assessed is comprehensively assessed, a corresponding management measure adjustment strategy is obtained, and construction of anti-money laundering work is facilitated and perfected.

Fig. 4 is a schematic structural diagram of a mechanism risk assessment device provided in an embodiment of the present application. As shown in fig. 4, the apparatus includes:

an obtaining module 401, configured to obtain historical risk data of a risk data item corresponding to an organization to be evaluated and historical control data of an anti-risk measure data item corresponding to the organization to be evaluated;

a first determining module 402, configured to determine, according to historical risk data, a first risk factor corresponding to a risk data item;

a second determining module 403, configured to determine, according to the historical control data, a second risk factor corresponding to the counter-risk measure data item;

and the fusion module 404 is configured to fuse the first prediction result and the second prediction result according to a preset data fusion rule to obtain a risk evaluation result of the to-be-evaluated mechanism, where the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor.

For example, the obtaining module 401 may perform the step S101 shown in fig. 1, the first determining module 402 may perform the step S102 shown in fig. 1, the second determining module 403 may perform the step S103 shown in fig. 1, and the fusing module 404 may perform the step S104 shown in fig. 1.

Optionally, the risk data item comprises a first risk data item. For example, the first risk data item may include a plurality of items of data information as money laundering risk indicators. In one example, the first risk data item may include one or more of regional and business scale information, customer information, business information, and channel information corresponding to the organization to be assessed.

The historical risk data acquired by the acquisition module 401 may include first historical data corresponding to the first risk data item, and the historical control data may include second historical data corresponding to the first counter-risk measure data item, wherein:

In an embodiment of the present application, the first prediction result may include a first predictor, and the first predictor may be determined by the first event risk factor as a preliminary money laundering risk prediction result. The second predictor may include a second predictor, which may be determined by the first event anti-risk factor, as a preliminary anti-money laundering control predictor. And performing fusion calculation on the first predictor result and the second predictor result to obtain a risk evaluation result of the mechanism to be evaluated on the anti-money laundering capacity.

a1. the situation of money laundering, terrorist financing and upstream crime in the region (registration place or operation place) of the organization to be evaluated, whether the organization is adjacent to overseas countries and regions where money laundering, terrorist financing or upstream crime and terrorist activity are active, or whether the organization belongs to countries and regions with higher risk;

b. The first customer information may include the following data information:

the method comprises the following steps that information such as the number and proportion of organ criminal inquiry, freezing, deduction and people bank investigation related to a client of an organization to be evaluated;

c. The service information may include the following data information:

d. The first channel information may include the following data information:

And all data item information in the region and operation scale information, the first customer information, the business information and the first channel information can be used as money laundering risk indexes to participate in the comprehensive money laundering risk evaluation of the organization to be evaluated.

In this embodiment of the application, each item of data information included in the first risk data item may be stored in one or more databases of corresponding servers, and the databases may store history data of all the first risk data items of the organization to be evaluated, and the history data of the first risk data item is stored in association with the organization number of the corresponding organization to be evaluated, so that the history data may be retrieved from the relevant storage database according to the organization number.

Or, the various data information can be recorded in the data storage area for calling in a manual entry mode.

In this embodiment of the application, the historical risk data acquired by the acquiring module 401 may include first historical data corresponding to the first risk data item, that is, historical data of the first risk data item in a historical year.

Optionally, after the obtaining module 401 obtains the first history data corresponding to the first risk data item, the first determining module 402 may determine a corresponding risk factor, in this embodiment of the present application, the first risk factor includes a first event risk factor, and the first determining module 402 specifically includes a first determining sub-module 4021:

the first determining sub-module 4021 is configured to determine a first event risk factor corresponding to the first risk data item according to the first historical data.

After the first determining sub-module 4021 determines the first event risk factor corresponding to the first risk data item, it may be used to predict the money laundering risk. Optionally, in this embodiment of the application, the first predicted data result may include a first predictor result, and the method may further include the first calculating module 405:

the first calculating module 405 is configured to perform weighted calculation on the first event risk factor according to a preset weighting rule to obtain a first predictor result.

In the information of the plurality of data items included in the first risk data item, the risk factor corresponding to each data item can be weighted and calculated according to a preset weighting rule, and the total money laundering risk obtained is divided into a first predictor result.

In order to quantify the remaining money laundering risk of the institution to be evaluated, optionally, in the embodiment of the present application, the effectiveness of the anti-money laundering measure of the institution also needs to be evaluated. The counter-risk measure data items acquired by the acquisition module 401 thus include a first counter-risk measure data item. For example, the first anti-risk measure data item may include control measure information corresponding to the organization to be assessed, where the control measure information includes control measure information items for one or more of the region and business scale, the customer group, the product business and the channel, and the first anti-risk measure data item may specifically include:

In this embodiment of the application, the historical control data acquired by the acquiring module 401 may include second historical data corresponding to the first counter-risk measure data item, that is, historical data of the first counter-risk measure data item in historical years.

Optionally, after the obtaining module 401 obtains the second history data corresponding to the first anti-risk measure data item, a second determining module may determine a corresponding risk factor, in this embodiment of the present application, the second risk factor includes a first event anti-risk factor, and the second determining module 403 specifically includes the second determining sub-module 4031:

and the second determining submodule 4031 is configured to determine, according to the second historical data, a first event anti-risk factor corresponding to the first anti-risk measure data item.

After the first event anti-risk factor corresponding to the first anti-risk measure data item is determined by the second determining sub-module 4031, the first event anti-risk factor can be used for predicting the anti-money laundering risk. Optionally, in this embodiment of the application, the second predicted data result may include a second predicted sub-result, and the apparatus may further include the second calculating module 406:

and the second calculating module 406 is configured to perform weighted calculation on the first event anti-risk factor according to a preset weighting rule to obtain a second predictor result.

In the information of the plurality of data items included in the anti-money laundering risk number measure items, the risk factor corresponding to each data item can be weighted and calculated according to a preset weighting rule, and the total money laundering risk is divided into a second predictor.

After the first predictor result and the second predictor result are obtained, the fusion module 404 can perform fusion calculation on the two predictor results, so as to determine the evaluation result of the mechanism to be evaluated in the aspect of anti-money laundering risk capability. Optionally, in this embodiment of the application, the fusion module 404 fuses the first prediction result and the second prediction result according to a preset data fusion rule to obtain a risk assessment result of the to-be-assessed mechanism, which may specifically include:

and the first fusion submodule 4041 is configured to fuse the first predictor result and the second predictor result according to a preset data fusion rule to obtain an anti-money laundering risk assessment result of the to-be-assessed institution.

Illustratively, the two predictors can be fused according to the data fusion rule of table 1.

In order to objectively and accurately evaluate the remaining sanction compliance risks of the organization to be evaluated, optionally, in the embodiment of the application, the corresponding sanction compliance evaluation result is obtained by extracting and processing data information of the sanction risks and sanction control measures. Illustratively, the risk data item includes a second risk data item. For example, the second risk data item may include a plurality of items of data information as the sanction risk indicator. In one example, the second risk data item may include: and the second channel information, the second customer information and the region information of the organization to be evaluated.

The historical risk data acquired by the acquisition module 401 may include third historical data corresponding to the second risk data item, and the historical control data may include fourth historical data corresponding to the second counter-risk measure data item, wherein:

a. the second channel information may include the following data information:

b. the second customer information may include the following data information:

In this embodiment of the application, the historical risk data acquired by the acquiring module 401 may include third historical data corresponding to the second risk data item, that is, historical data of the second risk data item in a historical year.

Optionally, after the obtaining module 401 obtains the third history data corresponding to the second risk data item, the first determining module 402 may determine a corresponding risk factor, in this embodiment of the application, the first risk factor includes a second event risk factor, and the first determining module 402 may specifically include:

the third determining submodule 4022 is configured to determine a second event risk factor corresponding to the second risk data item according to the third history data.

After the second event risk factor corresponding to the second risk data item is determined by the third determining sub-module 4022, it may be used to predict sanction compliance risk. Optionally, in this embodiment of the application, the first predicted data result may include a third predicted sub-result, and the apparatus may further include:

and the third calculating module 407 is configured to perform weighted calculation on the second event risk factor according to a preset weighting rule to obtain a third predictor result.

In order to quantify the remaining sanction risk of the organization to be evaluated, optionally, in the embodiment of the present application, the effectiveness of sanction control measures of the organization needs to be evaluated. The counter-risk measure data items acquired by the acquisition module 401 thus include a second counter-risk measure data item. For example, the second anti-risk measure data item may include sanction compliance control measure information corresponding to the organization to be evaluated, and the sanction compliance control measure information may include organization architecture and personnel configuration information of the organization to be evaluated, money laundering risk evaluation information of the customer, financial sanction system construction, internal communication and information transfer degree, customer identification flow and completion degree, record preservation condition, sanction list screening and monitoring measures, information privacy measures, guarantee propulsion mechanism, and other information.

It should be understood that the historical data information corresponding to the second counter-risk measure data item may be stored in one or a database so as to be retrieved according to the organization number of the organization to be evaluated.

In this embodiment of the application, the historical control data acquired by the acquiring module 401 may include fourth historical data corresponding to the second counter-risk measure data item, that is, historical data of the second counter-risk measure data item in historical years.

Optionally, after the obtaining module 401 obtains the fourth historical data corresponding to the second counter risk measure data item, the second determining module 403 may determine a corresponding risk factor, in this embodiment of the application, the second risk factor includes a second event counter risk factor, and the second determining module 403 specifically includes:

and a fourth determining submodule 4032, configured to determine, according to the fourth historical data, a second event anti-risk factor corresponding to the sanction control risk data item.

After the second event anti-risk factor corresponding to the second anti-risk measure data item is determined by the fourth determination sub-module 4032, the second event anti-risk factor can be used for predicting the sanction control effectiveness. Optionally, in this embodiment of the application, the second predicted data result may include a fourth sub-result predictor, and the apparatus may further include:

the fourth calculating module 408 is configured to perform weighted calculation on the second event anti-risk factor according to a preset weighting rule, so as to obtain a fourth predictor result.

After the third predictor result and the fourth predictor result are obtained, the two predictor results can be subjected to fusion calculation through the obtaining module 401, so that the evaluation result of the mechanism to be evaluated in the aspect of sanction compliance risk capability is determined. Optionally, in this embodiment of the application, the fusion module 404 may specifically include:

and the second fusion submodule 4042 is configured to fuse the third predictor result and the fourth predictor result according to a preset data fusion rule to obtain a sanction compliance risk assessment result of the to-be-assessed organization.

The data fusion rule can also be used for performing fusion calculation on the third predictor result and the fourth predictor result so as to fully measure the sanction risk of the mechanism to be evaluated and the comprehensive score of the effectiveness of sanction control measures. Illustratively, the two predictors can be fused according to the data fusion rule of table 2.

Optionally, the apparatus may further include:

a generating module 409, configured to invoke a risk assessment report template according to the anti-money laundering risk assessment result and the sanction compliance risk assessment result, and generate a corresponding risk assessment report; and

and an output module 410 for saving and outputting the risk assessment report.

f. carrying out risk prompt aiming at the problems found by evaluation;

h. other measures that can effectively control the risk.

It should be noted that all relevant contents of each step related to the above method embodiment may be referred to the functional description of the corresponding functional module, and the corresponding technical effect can be achieved, and for brevity, no further description is provided herein.

Fig. 5 is a schematic hardware structure diagram of an organization risk assessment device provided in an embodiment of the present application. As shown in fig. 5, the apparatus 500 includes: a processor 501 and a memory 502 storing computer program instructions;

the processor 501, when executing the computer program instructions, implements any of the institutional risk assessment methods described in the embodiments above.

The facility risk assessment device 500 may include a processor 501 and a memory 502 storing computer program instructions.

Specifically, the processor 501 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 502 may include mass storage for data or instructions. By way of example, and not limitation, memory 502 may include a Hard Disk Drive (HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 502 may include removable or non-removable (or fixed) media, where appropriate. The memory 502 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 502 is non-volatile solid-state memory.

The memory may include Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the methods according to an aspect of the application.

The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement any one of the mechanism risk assessment methods in the above embodiments.

Exemplarily, fig. 6 shows a hardware structure diagram of a terminal device in an embodiment of the present application. As shown in fig. 6, the terminal device may include: a back-end server 601, a data exchange interface 602, a front-end server 603, a user interface 604, and a communication bus 605.

Wherein the content of the first and second substances,

the communication bus 605 is used for realizing connection communication among the back-end server 601, the data exchange interface 602, the front-end server 603 and the user interface 604.

The user interface 604 may include a display screen, an input unit such as a keyboard, a tablet, a stylus, etc.

In the terminal device shown in fig. 6, the user interface 604 is mainly used for data communication with each other external terminal; the data exchange interface 602 is mainly used for connecting to a backend server and performing data communication with the backend server.

In this embodiment, the terminal device is deployed separately from the front end and the back end, and the back end server 601 deploys a back end project program, which may be any one of the mechanism risk assessment methods in the embodiments described above.

The back-end server 601 may call the relevant data of the database and the data input by the user interface to perform risk assessment, and the assessment result is transmitted to the front-end server 603 through the data interaction interface 602, and the front-end server further displays the data on the user interface 604.

In addition, in combination with the organization risk assessment method in the above embodiment, the embodiment of the present application further provides a computer storage medium to implement. The computer storage medium has stored thereon computer program instructions that, when executed by a processor, implement any of the above described institutional risk assessment methods.

Furthermore, the present application also provides a computer program product, where instructions in the computer program product, when executed by a processor of an electronic device, enable the electronic device to execute any one of the mechanism risk assessment methods in the foregoing embodiments.

It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.

The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus, and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.

Claims

1. An institutional risk assessment method, comprising:

determining a first risk factor corresponding to the risk data item according to the historical risk data;

determining a second risk factor corresponding to the counter-risk measure data item according to the historical control data;

and according to a preset data fusion rule, fusing a first prediction result and a second prediction result to obtain a risk evaluation result of the mechanism to be evaluated, wherein the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor.

2. The method of claim 1, wherein the risk data item comprises a first risk data item, and wherein historical risk data comprises first historical data corresponding to the first risk data item; the first risk factor comprises a first event risk factor;

determining a first risk factor corresponding to the risk data item according to the historical risk data includes:

3. The method of claim 2, wherein the counter-risk measure data item comprises a first counter-risk measure data item, and the historical control data comprises second historical data corresponding to the first counter-risk measure data item; the second risk factor comprises a first event anti-risk factor;

the determining a second risk factor corresponding to the counter-risk measure data item according to the historical control data includes:

4. The method of claim 3, wherein the first predicted outcome comprises a first predictor outcome and the second predicted outcome comprises a second predictor outcome;

before the first prediction result and the second prediction result are fused according to a preset data fusion rule to obtain a risk evaluation result of the structure to be evaluated, the method comprises the following steps:

according to a preset weighting rule, carrying out weighting calculation on the first event risk factor to obtain the first predictor result; and

and according to the preset weighting rule, carrying out weighting calculation on the first event anti-risk factor to obtain the second predictor result.

5. The method according to claim 3 or 4, wherein the first risk data item comprises one or more of region and business scale information, first customer information, business information and first channel information corresponding to the organization to be assessed;

the first anti-risk measure data item comprises control measure information corresponding to the organization to be evaluated, and the control measure information comprises control measure information for one or more of the region, the operation scale, the customer group, the product business and the channel.

6. The method of claim 1, wherein the risk data item comprises a second risk data item, and the historical risk data comprises third historical data corresponding to the second risk data item; the first risk factor comprises a second event risk factor;

7. The method of claim 6, wherein the counter-risk measure data item comprises a second counter-risk measure data item, and the historical control data comprises fourth historical data corresponding to the second counter-risk measure data item; the second risk factor comprises a second event anti-risk factor;

the determining a second risk factor corresponding to the counter risk measure risk item according to the historical control data includes:

8. The method of claim 7, wherein the first predictor comprises a third predictor and the second predictor comprises a fourth predictor;

performing weighted calculation on the second event risk factor according to a preset weighted rule to obtain a third predictor result; and

and performing weighted calculation on the second event anti-risk factor according to a preset weighting rule to obtain the fourth predictor result.

9. An institutional risk assessment device, the device comprising:

the second determining module is used for determining a second risk factor corresponding to the counter-risk measure data item according to the historical control data;

and the fusion module is used for fusing a first prediction result and a second prediction result according to a preset data fusion rule to obtain a risk evaluation result of the mechanism to be evaluated, wherein the first prediction result is determined according to the first risk factor, and the second prediction result is determined according to the second risk factor.

10. An institutional risk assessment device, the device comprising: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the institutional risk assessment method of any one of claims 1-8.

11. A computer storage medium having computer program instructions stored thereon that, when executed by a processor, implement the institutional risk assessment method of any one of claims 1-8.

12. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the institutional risk assessment method of any one of claims 1-8.