Financial risk monitoring system based on distributed setting

Abstract

The invention provides a financial risk monitoring system based on distributed setting, and the system comprises a data collection layer which is used for collecting data from a plurality of data sources inside and outside a financial institution, and carrying out the preprocessing of the collected data; the data storage layer is used for storing the collected and preprocessed data; the calculation and analysis layer is built based on a distributed calculation framework, integrates various financial risk assessment models and is used for analyzing the preprocessed data and calculating comprehensive risk indexes in real time; the risk early warning and decision-making layer is used for monitoring the calculated risk indexes according to a preset risk index threshold value, triggering a risk early warning mechanism when the risk indexes exceed the threshold value, and providing decision-making support for the management layer of the financial institution; and the user interaction layer is used for providing risk monitoring data query, risk index report checking, risk early warning information acquisition, risk parameter setting and risk model adjustment functions for users. According to the invention, the efficiency and level of financial institution risk management are improved.

Description

Technical Field

[0001] The present invention belongs to the field of financial technology, and specifically relates to a financial risk monitoring system based on a distributed setting. Background Art

[0002] With the rapid development and digital transformation of the financial industry, the scale and complexity of financial business are increasing, and the risks they face are becoming increasingly diverse. Traditional centralized financial risk monitoring systems have exposed problems such as limited processing capacity, insufficient scalability, and high single point failure risks when processing massive data, dealing with high concurrent transactions, and complex risk scenarios. They cannot meet the real-time, accuracy, and reliability requirements of modern financial business for risk monitoring. Therefore, it is of great practical significance to develop an efficient, scalable, and reliable distributed financial risk monitoring system. Summary of the invention

[0003] The purpose of the present invention is to provide a financial risk monitoring system based on a distributed setting to address the defects of the traditional centralized system, achieve comprehensive and real-time monitoring of financial risks, provide scientific decision-making support for financial institutions, and reduce financial risks.

[0004] The technical solution of the present invention is as follows:

[0005] A financial risk monitoring system based on a distributed setting, comprising:

[0006] Data collection layer: used to collect data from multiple data sources inside and outside the financial institution and pre-process the collected data;

[0007] Data storage layer: used to store collected and pre-processed data;

[0008] Computing and analysis layer: Based on the distributed computing framework, it integrates multiple financial risk assessment models to analyze pre-processed data and calculate comprehensive risk indicators in real time;

[0009] Risk warning and decision-making layer: monitor the calculated risk indicators according to the preset risk indicator thresholds, trigger the risk warning mechanism when the risk indicators exceed the thresholds, and provide decision support for the management of financial institutions;

[0010] User interaction layer: used to provide users with risk monitoring data query, risk indicator report viewing, risk warning information acquisition, risk parameter setting and risk model adjustment functions.

[0011] Furthermore, the internal data source includes business data generated by the core business system, the external data source includes financial market data, macroeconomic data and third-party credit rating data, and the preprocessing includes data cleaning, data standardization and data sampling;

[0012] Furthermore, the data storage layer includes a distributed file system and a distributed database, wherein the distributed file system is used to store original data and intermediate processing results, and the distributed database is used to store structured business data and risk indicator data;

[0013] Furthermore, the risk warning and decision-making layer specifically includes:

[0014] Risk threshold setting module: used to set the thresholds of various risk indicators according to the risk tolerance of financial institutions, regulatory requirements and historical business data. The thresholds are dynamically adjusted according to the market environment and business conditions;

[0015] Risk monitoring module: Receives risk indicators output by the calculation and analysis layer in real time, compares them with the preset risk indicator thresholds, and determines whether there is a risk exceeding the limit;

[0016] Risk warning trigger module: When the risk monitoring module finds that the risk indicator exceeds the threshold, the risk warning mechanism is immediately triggered.

[0017] Furthermore, the financial risk assessment model includes a credit risk assessment model, a market risk assessment model and a liquidity risk assessment model;

[0018] The credit risk assessment model is used to assess the default risk of a borrower or a counterparty;

[0019] The market risk assessment model is used to assess the risks brought about by price fluctuations in financial markets;

[0020] The liquidity risk assessment model is used to assess the liquidity and liquidity of assets in the market.

[0021] Furthermore, the risk warning trigger module specifically includes: the first-level warning corresponds to high risk, using multiple warning methods to notify and require immediate processing at the same time; the second-level warning corresponds to medium risk, using some warning methods to notify and require attention; the third-level warning corresponds to low risk, and prompts are given through system pop-up windows.

[0022] Furthermore, the credit risk assessment model adopts the KMV model, which calculates the default distance by the following formula:

[0023]

[0024] Among them, V A is the value of enterprise assets, D is the face value of enterprise debt, σ A is the volatility of the enterprise's asset value; the default probability is calculated by the relationship between the default distance and the standard normal distribution function B(.):

[0025] PD=N(-Dd)

[0026] Among them, PD is the probability of default.

[0027] Furthermore, the market risk assessment model uses the variance-covariance method to calculate the risk value, and the formula is:

[0028]

[0029] Among them, z 1-c is the quantile corresponding to the confidence level 1-c under the standard normal distribution, P 0 is the initial value of the portfolio, σ P Standard deviation of portfolio returns, Δ t For the holding period.

[0030] Furthermore, the liquidity risk assessment model uses the liquidity coverage ratio indicator for assessment, and the calculation formula is:

[0031]

[0032] When LCR ≥ 100%, it indicates that the financial institution has sufficient liquidity in the short term of 30 days to cope with the net cash outflow under the stress scenario.

[0033] Furthermore, when the calculation and analysis layer conducts a comprehensive evaluation of various risk indicators, the weighted average method is used to calculate the comprehensive risk indicator, and the formula is:

[0034]

[0035] Where k is the number of risk indicators, w i is the weight of the ith risk indicator, and RI i is the calculated value of the ith risk indicator.

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] The integration of multiple financial risk assessment models enables comprehensive assessment and real-time monitoring of various financial risks.

[0038] The risk warning and hierarchical warning and decision support functions of the decision-making level help financial institutions to detect and respond to risks in a timely manner and reduce losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings generally illustrate various embodiments by way of example and not limitation, and together with the description and claims, serve to illustrate the embodiments of the invention. Where appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive embodiments of the present apparatus or method.

[0040] Figure 1 The overall architecture diagram of the financial risk monitoring system based on a distributed setting of the present invention is shown. DETAILED DESCRIPTION

[0041] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] like Figure 1 As shown, an embodiment of the present invention provides a financial risk monitoring system based on a distributed setting, including:

[0043] Data collection layer: used to collect data from multiple data sources inside and outside the financial institution and pre-process the collected data.

[0044] Multi-source data access: Internal data sources include business data generated by core business systems (such as trading systems, credit systems, clearing systems, etc.), including transaction records, customer information, account balances, credit approval information, etc.; external data sources include financial market data (such as prices, trading volumes, volatility, etc. of stocks, bonds, futures, etc.), macroeconomic data (such as GDP growth rate, inflation rate, interest rate trends, etc.) and third-party credit rating data, etc. By adapting to different data interfaces and protocols (such as API, FTP, database connection, etc.), efficient collection of various structured and unstructured data is achieved.

[0045] Data preprocessing: preliminary processing of the collected data, including data cleaning, removal of duplicate, erroneous, and incomplete data; data standardization, unifying data in different formats into a standard format recognizable by the system; data sampling, reasonable sampling of massive data, reducing the pressure of subsequent processing while ensuring the representativeness of the data.

[0046] Data storage layer: used to store collected and processed data safely and efficiently.

[0047] Distributed File System: Adopt distributed file systems such as Ceph and GlusterFS to store massive amounts of raw data and intermediate processing results dispersedly on multiple nodes. Through data redundancy backup and sharding technologies, ensure the reliability and availability of data. Even if individual nodes fail, data can still be obtained from other replicas without affecting the normal operation of the system. At the same time, the distributed file system has good scalability and can easily add storage nodes according to the growth of data volume.

[0048] Distributed Database: Select distributed databases such as TiDB and CockroachDB to store structured business data and risk indicator data. The distributed database supports horizontal scaling of data and can improve the read and write performance of data by adding nodes to meet the data storage and query requirements in high-concurrency transaction scenarios. In addition, it also has strong transaction processing capabilities to ensure data consistency and integrity.

[0049] Computing and Analysis Layer: Built based on a distributed computing framework, integrating a variety of financial risk assessment models for analyzing the collected data and calculating various risk indicators in real time.

[0050] Distributed Computing Framework: Adopt the Apache Spark distributed computing framework to decompose complex computing tasks into multiple subtasks and execute them in parallel on multiple nodes in the cluster. These frameworks support two modes: real-time stream processing and batch processing, and can efficiently process large-scale data.

[0051] Integration of Financial Risk Assessment Models: Integrate a variety of professional financial risk assessment models, including credit risk assessment models, market risk assessment models, and liquidity risk assessment models.

[0052] The credit risk assessment model adopts the KMV model, and the model calculates the distance to default through the following formula:

[0053]

[0054] where V A is the enterprise asset value, D is the face value of the enterprise debt, and σ A is the volatility of the enterprise asset value; the default probability is calculated through the relationship between the distance to default and the standard normal distribution function B(.):

[0055] PD = N(-DD)

[0056] where PD is the default probability.

[0057] The market risk assessment model adopts the variance-covariance method to calculate the value at risk, and the formula is:

[0058]

[0059] where z 1-c is the quantile corresponding to the confidence level 1 - c under the standard normal distribution, P 0 is the initial value of the portfolio, σ P is the standard deviation of the portfolio return rate, Δ t is the holding period.

[0060] The liquidity risk assessment model uses the liquidity coverage ratio indicator for assessment, and the calculation formula is:

[0061]

[0062] When LCR ≥ 100%, it indicates that the financial institution has sufficient liquidity within 30 days in the short term to cope with the net cash outflow under stress scenarios.

[0063] When the calculation and analysis layer comprehensively evaluates various risk indicators, the weighted average method is used to calculate the comprehensive risk indicator, and the formula is:

[0064]

[0065] where k is the number of risk indicators, w i is the weight of the i-th risk indicator, and RI i is the calculated value of the i-th risk indicator.

[0066] Risk warning and decision-making layer: According to the preset risk indicator thresholds, monitor the calculated risk indicators. When the risk indicators exceed the thresholds, trigger the risk warning mechanism and provide decision-making support for the management of financial institutions.

[0067] Risk threshold setting: Financial institutions set the thresholds of various risk indicators in the system according to their own risk tolerance, business characteristics and regulatory requirements. The thresholds can be dynamically adjusted according to different business scenarios and risk types to ensure the accuracy and timeliness of risk warnings.

[0068] Risk warning mechanism: When the calculated risk indicators exceed the preset thresholds, the system automatically triggers the risk warning mechanism. The warning methods include SMS notification, email reminder, system pop-up prompt, etc. At the same time, display the risk information prominently on the visualization interface, including risk type, risk level, impact range, etc. The warning information can also be customized and pushed according to different user roles to ensure that relevant personnel can obtain key information in a timely manner.

[0069] Decision Support System: Based on the risk assessment results and early warning information, it provides decision support for the management of financial institutions. The system provides simulation and evaluation functions for various risk response strategies. By predicting and analyzing the changes in risk indicators under different strategies, it helps the management select the optimal risk response plan, such as adjusting the investment portfolio, tightening credit policies, increasing liquidity reserves, etc. At the same time, the system also provides historical case analysis and expert advice for reference in decision-making.

[0070] User Interaction Layer: It is used to provide users with functions such as querying risk monitoring data, viewing risk indicator reports, obtaining risk early warning information, setting risk parameters, and adjusting risk models.

[0071] Web Interface: It provides a Web-based user interface for risk management personnel, business personnel, and management. The interface is designed to be simple and intuitive, making it easy to operate. Users can conveniently query risk monitoring data, view risk indicator reports, and obtain risk early warning information through this interface. At the same time, users can also perform operations such as setting risk parameters and adjusting risk models on the interface, and the system updates the calculation results in real time to ensure the accuracy and flexibility of risk monitoring.

[0072] Mobile Application: Develop a mobile application that supports iOS and Android platforms. The mobile application has a simple and easy-to-use interface and real-time push function, enabling relevant personnel to obtain risk monitoring information anytime and anywhere. Users can view the real-time changes in risk indicators, receive risk early warning notifications, and perform simple query and setting operations on mobile devices, improving work efficiency and response speed.

[0073] The above are only the preferred specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. A financial risk monitoring system based on a distributed setting, characterized in that: include: Data collection layer: used to collect data from multiple data sources inside and outside the financial institution and pre-process the collected data; Data storage layer: used to store collected and pre-processed data; Computing and analysis layer: Based on the distributed computing framework, it integrates multiple financial risk assessment models to analyze pre-processed data and calculate comprehensive risk indicators in real time; Risk warning and decision-making layer: monitor the calculated risk indicators according to the preset risk indicator thresholds, trigger the risk warning mechanism when the risk indicators exceed the thresholds, and provide decision support for the management of financial institutions; User interaction layer: used to provide users with risk monitoring data query, risk indicator report viewing, risk warning information acquisition, risk parameter setting and risk model adjustment functions.

2. The distributed financial risk monitoring system according to claim 1 is characterized in that: The internal data source includes business data generated by the core business system, the external data source includes financial market data, macroeconomic data and third-party credit rating data, and the preprocessing includes data cleaning, data standardization and data sampling.

3. The distributed financial risk monitoring system according to claim 1, characterized in that: The data storage layer includes a distributed file system and a distributed database. The distributed file system is used to store original data and intermediate processing results, and the distributed database is used to store structured business data and risk indicator data.

4. The distributed financial risk monitoring system according to claim 1, characterized in that: The risk warning and decision-making layer specifically includes: Risk threshold setting module: used to set the thresholds of various risk indicators according to the risk tolerance of financial institutions, regulatory requirements and historical business data. The thresholds are dynamically adjusted according to the market environment and business conditions; Risk monitoring module: Receives risk indicators output by the calculation and analysis layer in real time, compares them with the preset risk indicator thresholds, and determines whether there is a risk exceeding the limit; Risk warning trigger module: When the risk monitoring module finds that the risk indicator exceeds the threshold, the risk warning mechanism is immediately triggered.

5. The distributed financial risk monitoring system according to claim 1, characterized in that: The financial risk assessment model includes a credit risk assessment model, a market risk assessment model and a liquidity risk assessment model; The credit risk assessment model is used to assess the default risk of a borrower or a counterparty; The market risk assessment model is used to assess the risks brought about by price fluctuations in the financial market; The liquidity risk assessment model is used to assess the liquidity and liquidity of assets in the market.

6. The distributed financial risk monitoring system according to claim 4, characterized in that: The risk warning trigger module specifically includes: the first-level warning corresponds to high risk, and multiple warning methods are used to notify and require immediate processing at the same time; the second-level warning corresponds to medium risk, and some warning methods are used to notify and require attention; the third-level warning corresponds to low risk, and prompts are given through system pop-up windows.

7. The distributed financial risk monitoring system according to claim 5, characterized in that: The credit risk assessment model adopts the KMV model, which calculates the default distance through the following formula: Among them, V A is the value of enterprise assets, D is the face value of enterprise debt, σ A is the volatility of the enterprise's asset value; the default probability is calculated by the relationship between the default distance and the standard normal distribution function B(.): PD=N(-DD) Among them, PD is the probability of default.

8. The distributed financial risk monitoring system according to claim 5, characterized in that: The market risk assessment model uses the variance-covariance method to calculate the risk value, and the formula is: Among them, z 1-c is the quantile corresponding to the confidence level 1-c under the standard normal distribution, P0 is the initial value of the portfolio, σ P Standard deviation of portfolio returns, Δ t For the holding period.

9. The distributed financial risk monitoring system according to claim 5, characterized in that: The liquidity risk assessment model uses the liquidity coverage ratio indicator for assessment, and the calculation formula is: When LCR ≥ 100%, it indicates that the financial institution has sufficient liquidity in the short term of 30 days to cope with the net cash outflow under the stress scenario.

10. The distributed financial risk monitoring system according to claim 5, characterized in that: When the calculation and analysis layer conducts a comprehensive assessment of various risk indicators, the weighted average method is used to calculate the comprehensive risk indicator. The formula is: Where k is the number of risk indicators, w i is the weight of the ith risk indicator, and RI i is the calculated value of the ith risk indicator.

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