A financial risk early warning system and method based on big data

Abstract

The present invention relates to the technical field of financial risk monitoring and early warning, and specifically discloses a financial risk early warning system and method based on big data, including: a processing module, which is used to obtain the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment based on all types of profit parameters, all types of asset parameters and all types of operation parameters of the financial institution at the current moment; a second judgment module, which is used to obtain the second risk judgment result of the financial institution at the current moment based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment; an early warning module, which is used to obtain the financial risk early warning result of the financial institution at the current moment based on the second risk judgment result of the financial institution at the current moment. The present invention realizes an overall assessment of the financial health status of the financial institution at the current moment, and provides timely financial risk early warning for the financial institution.

Description

Technical Field

[0001] The present invention relates to the technical field of financial risk monitoring and early warning, and in particular to a financial risk early warning system and method based on big data. Background Art

[0002] At present, in the context of increasingly complex and changing financial markets, financial institutions are facing risks from many aspects, including but not limited to credit risk and liquidity risk. These risks not only threaten the sound operation of financial institutions, but may also cause a chain reaction on the entire financial system and affect economic stability. Therefore, timely and accurate risk assessment and early warning of financial institutions have become an important part of maintaining financial stability, protecting the interests of investors, and promoting the healthy development of the economy. Traditional risk assessment methods are often difficult to fully and accurately capture and quantify these risks. Therefore, how to accurately judge whether financial institutions need to issue financial risk early warnings at the current moment has become an urgent issue.

[0003] However, the existing financial risk early warning system and method based on big data only accurately assess the loan risks in different regions, thereby reducing the problem of regional risk imbalance and business instability, and accurately assess the user's repayment ability by comprehensively analyzing personal credit and big data, thereby reducing the non-performing loan rate, but it only monitors and warns the financial risks of regions and individuals, and does not comprehensively assess the financial health of financial institutions at the current moment in terms of profitability, asset quality and operation of financial institutions, and does not provide timely financial risk early warning for financial institutions. For example, the patent with the publication number of "CN117011031A" and the name of the patent is "Intelligent Financial Risk Monitoring and Early Warning Platform Using Big Data Information Processing Technology", and its method includes the following steps: By fully considering regional characteristics, the platform can more accurately assess the loan risks in different regions, thereby reducing the problem of regional risk imbalance and business instability. Secondly, by comprehensively analyzing personal credit and big data, the platform can accurately assess the user's repayment ability, thereby reducing the non-performing loan rate, improving business efficiency and user satisfaction. Based on data-driven risk decision-making, the platform improves the decision-making accuracy of financial institutions, reduces uncertainty, and improves profitability. The intelligent early warning solution makes risk management more proactive and efficient, identifies risks in a timely manner, and reduces the possibility of risk expansion, thereby ensuring the stability of the business. The above patent will bring refined risk management and intelligent service experience as well as steady business development to the financial industry. However, the patent only reduces regional risk imbalances and business instability by accurately assessing loan risks in different regions, and accurately assesses users' repayment ability through comprehensive analysis of personal credit and big data, thereby reducing the non-performing loan rate. However, it only monitors and warns of regional and individual financial risks, and does not comprehensively assess the profitability, asset quality and operating conditions of financial institutions to comprehensively assess the financial health of financial institutions at the current moment, and does not provide timely financial risk warnings for financial institutions.

[0004] Therefore, the present invention proposes a financial risk early warning system and method based on big data. Summary of the invention

[0005] The present invention provides a financial risk early warning system and method based on big data, which is used to obtain the first risk judgment result of the financial institution at the current moment according to all profit parameters, asset parameters and operation parameters of the financial institution at the current moment, make a first judgment on the risk of the financial institution at the current moment, obtain the profitability degree value of the financial institution at the current moment according to all profit parameters of the financial institution at the current moment, and realize quantification of the profitability of the financial institution at the current moment, obtain the asset quality degree value of the financial institution at the current moment according to all asset parameters of the financial institution at the current moment, and realize quantification of the asset quality of the financial institution at the current moment, and obtain the operating condition degree value of the financial institution at the current moment according to all operation parameters of the financial institution at the current moment, and realize The operation status of the financial institution at the current moment is quantified, and the comprehensive financial assessment value of the financial institution at the current moment is obtained based on the profitability degree value, asset quality degree value and operation status degree value of the financial institution at the current moment, thereby realizing an overall assessment of the financial health status of the financial institution at the current moment. Based on the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments, the second risk judgment result of the financial institution at the current moment is obtained, and a second judgment is made on the risk of the financial institution at the current moment. Based on the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment, the financial risk warning result of the financial institution at the current moment is accurately obtained, thereby realizing the provision of timely financial risk warnings for financial institutions to avoid or reduce potential losses.

[0006] The present invention provides a financial risk early warning system based on big data, comprising:

[0007] A first judgment module, used to obtain a first risk judgment result of the financial institution at the current moment based on all profit parameters, all asset parameters and all operation parameters of the financial institution at the current moment;

[0008] A processing module, used to obtain the profitability degree value of the financial institution at the current moment based on all the profit-like parameters of the financial institution at the current moment, obtain the asset quality degree value of the financial institution at the current moment based on all the asset-like parameters of the financial institution at the current moment, and obtain the operating condition degree value of the financial institution at the current moment based on all the operating parameters of the financial institution at the current moment;

[0009] A second judgment module is used to obtain a comprehensive financial assessment value of the financial institution at the current moment based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, and obtain a second risk judgment result of the financial institution at the current moment based on the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments;

[0010] The early warning module is used to obtain the financial risk early warning result of the financial institution at the current moment based on the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment.

[0011] Preferably, the first judgment module of the financial risk early warning system based on big data includes:

[0012] The acquisition submodule is used to obtain all profit parameters, all asset parameters and all operating parameters of the financial institution at the current moment, wherein all profit parameters include the amount of newly added liabilities, operating income and operating expenses, all asset parameters include the amount of newly added loans, the amount of newly added non-performing loans and the balance of loan loss reserves, and all operating parameters include the number of loan applications, the number of newly added non-performing loans and the number of newly added loans;

[0013] The first judgment submodule is used to obtain the first risk judgment result of the financial institution at the current moment based on all profit parameters, all asset parameters and all operation parameters of the financial institution at the current moment.

[0014] Preferably, the first judgment submodule of the financial risk early warning system based on big data includes:

[0015] A judgment parameter extraction unit, used to use the newly added debt amount, newly added non-performing loan amount and newly added number of non-performing loans of the financial institution at the current moment as the judgment parameter of the financial institution at the current moment;

[0016] The first judgment unit is used to judge whether all judgment parameters of the financial institution at the current moment are less than the corresponding judgment parameter threshold. If so, the qualified is regarded as the first risk judgment result of the financial institution at the current moment; otherwise, the unqualified is regarded as the first risk judgment result of the financial institution at the current moment.

[0017] Preferably, in the financial risk early warning system based on big data, the processing module obtains the profitability value of the financial institution at the current moment based on all the class profitability parameters of the financial institution at the current moment, including:

[0018]

[0019] Among them, β is the profitability value of the financial institution at the current moment, z is the operating income of the financial institution at the current moment, c is the amount of new liabilities of the financial institution at the current moment, v is the operating expenses of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0020] Preferably, in the financial risk early warning system based on big data, the processing module obtains the asset quality value of the financial institution at the current moment based on all asset class parameters of the financial institution at the current moment, including:

[0021]

[0022] Among them, γ is the asset quality level of the financial institution at the current moment, h is the new loan amount of the financial institution at the current moment, g is the loan loss reserve balance of the financial institution at the current moment, b is the new non-performing loan amount of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0023] Preferably, in the financial risk early warning system based on big data, the processing module obtains the operating status value of the financial institution at the current moment based on all the operating parameters of the financial institution at the current moment, including:

[0024]

[0025] Among them, δ is the operating status value of the financial institution at the current moment, k is the number of loan applications of the financial institution at the current moment, w is the number of new loans of the financial institution at the current moment, r is the number of new non-performing loans of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0026] Preferably, the second judgment module of the financial risk early warning system based on big data includes:

[0027] The evaluation calculation submodule is used to obtain the comprehensive financial evaluation value of the financial institution at the current moment based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, that is:

[0028]

[0029] Among them, θ is the comprehensive financial evaluation value of the financial institution at the current moment, β is the profitability value of the financial institution at the current moment, γ is the asset quality value of the financial institution at the current moment, and δ is the operating condition value of the financial institution at the current moment, (β,γ,δ) max is the maximum value among the profitability, asset quality and operating performance of the financial institution at the current moment, (β,γ,δ) min is the minimum value among the profitability, asset quality and operating performance of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718;

[0030] The second judgment submodule is used to obtain a second risk judgment result of the financial institution at the current moment based on the comprehensive financial evaluation value of the financial institution at the current moment and the comprehensive financial evaluation values ​​at all evaluation moments.

[0031] Preferably, the second judgment submodule of the financial risk early warning system based on big data includes:

[0032] An evaluation time determination unit, used to take a preset time period before the current time as the evaluation time period of the current time, and uniformly sample the evaluation time period of the current time based on a preset sampling number to obtain all sampling moments, and take all sampling moments as all evaluation moments of the current time;

[0033] An analysis unit, used to determine whether the comprehensive financial evaluation value of each evaluation moment of the financial institution at the current moment is greater than or equal to the comprehensive financial evaluation value of the financial institution at the current moment, and if so, the corresponding evaluation moment is regarded as the first evaluation moment of the current moment, otherwise, the corresponding moment is regarded as the second evaluation moment of the current moment;

[0034] The second judgment unit is used to take the quotient between the number of all first evaluation moments and the number of all second evaluation moments at the current moment as the judgment quotient value at the current moment, and judge whether the judgment quotient value at the current moment is greater than the preset judgment quotient value. If so, qualified is regarded as the second risk judgment result of the financial institution at the current moment; otherwise, unqualified is regarded as the second risk judgment result of the financial institution at the current moment.

[0035] Preferably, the financial risk early warning system based on big data, the early warning module, includes:

[0036] The early warning analysis submodule is used to treat the need for alarm as the financial early warning analysis result at the current moment when the first risk judgment result or the second risk judgment result of the financial institution at the current moment is unqualified, and treat the need for alarm as the financial early warning analysis result at the current moment when both the first risk judgment result and the second risk judgment result of the financial institution at the current moment are qualified;

[0037] The early warning control submodule is used to control the preset alarm device to sound an alarm if the financial early warning analysis result at the current moment indicates that an alarm is required.

[0038] The present invention provides a financial risk early warning method based on big data, which is applied to any one of the financial risk early warning systems based on big data in Embodiments 1 to 9, comprising:

[0039] S1: Based on all profit parameters, asset parameters and operation parameters of the financial institution at the current moment, obtain the first risk judgment result of the financial institution at the current moment;

[0040] S2: Based on all profit parameters of the financial institution at the current moment, obtain the profitability value of the financial institution at the current moment; based on all asset parameters of the financial institution at the current moment, obtain the asset quality value of the financial institution at the current moment; based on all operating parameters of the financial institution at the current moment, obtain the operating condition value of the financial institution at the current moment;

[0041] S3: Based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, obtain the comprehensive financial evaluation value of the financial institution at the current moment, and based on the comprehensive financial evaluation value of the financial institution at the current moment and the comprehensive financial evaluation values ​​at all evaluation moments, obtain the second risk judgment result of the financial institution at the current moment;

[0042] S4: Based on the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment, obtain the financial risk warning result of the financial institution at the current moment.

[0043] The beneficial effects of the present invention compared with the prior art are as follows: based on all types of profit parameters, all types of asset parameters and all types of operating parameters of the financial institution at the current moment, the first risk judgment result of the financial institution at the current moment is obtained, and the risk of the financial institution at the current moment is first judged; based on all types of profit parameters of the financial institution at the current moment, the profitability degree value of the financial institution at the current moment is obtained, and the profitability of the financial institution at the current moment is quantified; based on all types of asset parameters of the financial institution at the current moment, the asset quality degree value of the financial institution at the current moment is obtained, and the asset quality of the financial institution at the current moment is quantified; based on all types of operating parameters of the financial institution at the current moment, the operating condition degree value of the financial institution at the current moment is obtained, and the financial institution is quantified. The operating conditions of the financial institution at the current moment are quantified, and the comprehensive financial assessment value of the financial institution at the current moment is obtained according to the profitability degree value, asset quality degree value and operating condition degree value of the financial institution at the current moment, thereby realizing an overall assessment of the financial health status of the financial institution at the current moment, and according to the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments, the second risk judgment result of the financial institution at the current moment is obtained, and a second judgment is made on the risk of the financial institution at the current moment, and according to the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment, the financial risk early warning result of the financial institution at the current moment is accurately obtained, thereby realizing providing timely financial risk early warning for the financial institution and avoiding or reducing potential losses.

[0044] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structure specifically pointed out in the written application documents.

[0045] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 This is a schematic diagram of a financial risk early warning system based on big data in an embodiment of the present invention;

[0048] Figure 2 This is a flow chart of a financial risk early warning method based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0050] Embodiment 1:

[0051] The present invention provides a financial risk early warning system based on big data. Figure 1 ,include:

[0052] A first judgment module, used to obtain a first risk judgment result of the financial institution at the current moment based on all profit parameters, all asset parameters and all operation parameters of the financial institution at the current moment;

[0053] A processing module, used to obtain the profitability degree value of the financial institution at the current moment based on all the profit-like parameters of the financial institution at the current moment, obtain the asset quality degree value of the financial institution at the current moment based on all the asset-like parameters of the financial institution at the current moment, and obtain the operating condition degree value of the financial institution at the current moment based on all the operating parameters of the financial institution at the current moment;

[0054] A second judgment module is used to obtain a comprehensive financial assessment value of the financial institution at the current moment based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, and obtain a second risk judgment result of the financial institution at the current moment based on the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments;

[0055] The early warning module is used to obtain the financial risk early warning result of the financial institution at the current moment based on the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment.

[0056] In this embodiment, the financial institution is a financial institution that uses a big data-based financial risk early warning system to perform risk early warning, such as a bank, a microfinance company, etc.

[0057] In this embodiment, all types of profit parameters are all parameters that can reflect the profitability information of a financial institution, wherein all types of profit parameters include the amount of newly added liabilities, operating income, and operating expenses.

[0058] In this embodiment, all asset-type parameters are all parameters that can reflect the asset quality of a financial institution, wherein all asset-type parameters include the amount of new loans, the amount of new non-performing loans, and the balance of loan loss reserves.

[0059] In this embodiment, all types of operating parameters are all parameters that can reflect the operating status of a financial institution, wherein all types of operating parameters include the number of loan applications, the number of new non-performing loans, and the number of new loans.

[0060] In this embodiment, the first risk judgment result of the financial institution at the current moment is the result of the first judgment on the risk of the financial institution at the current moment based on all profit parameters, all asset parameters and all operating parameters of the financial institution at the current moment, where the first risk judgment result includes qualified and unqualified.

[0061] In this embodiment, the profitability value of the financial institution at the current moment is a numerical value obtained based on all types of profitability parameters of the financial institution at the current moment, which can reflect the profitability of the financial institution at the current moment. The higher the profitability value, the stronger the profitability of the financial institution.

[0062] In this embodiment, the asset quality level value of the financial institution at the current moment is a numerical value obtained based on all asset-type parameters of the financial institution at the current moment, which can reflect the asset quality of the financial institution at the current moment. The higher the asset quality level value, the higher the asset quality of the financial institution.

[0063] In this embodiment, the operating status value of the financial institution at the current moment is a numerical value obtained based on all types of operating parameters of the financial institution at the current moment, which can reflect the operating status of the financial institution at the current moment. The higher the operating status value, the better the operating status of the financial institution.

[0064] In this embodiment, the comprehensive financial assessment value of a financial institution at the current moment is a value obtained by comprehensively assessing the financial health of the financial institution at the current moment in terms of profitability, asset quality and operating conditions of the financial institution.

[0065] In this embodiment, the second risk judgment result of the financial institution at the current moment is the result of a second judgment on the risk of the financial institution at the current moment based on the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments, wherein the second risk judgment result includes qualified and unqualified.

[0066] In this embodiment, the financial risk warning result of the financial institution at the current moment is that if the financial warning analysis result at the current moment requires an alarm, the preset alarm device is controlled to sound an alarm.

[0067] The beneficial effects of the above technology are as follows: based on all profit parameters, asset parameters and operating parameters of the financial institution at the current moment, the first risk judgment result of the financial institution at the current moment is obtained, and the risk of the financial institution at the current moment is judged for the first time; based on all profit parameters of the financial institution at the current moment, the profitability degree value of the financial institution at the current moment is obtained, and the profitability of the financial institution at the current moment is quantified; based on all asset parameters of the financial institution at the current moment, the asset quality degree value of the financial institution at the current moment is obtained, and the asset quality of the financial institution at the current moment is quantified; based on all operating parameters of the financial institution at the current moment, the operating condition degree value of the financial institution at the current moment is obtained, and the financial institution at the current moment is quantified. The operating conditions at the current moment are quantified, and the comprehensive financial assessment value of the financial institution at the current moment is obtained according to the profitability degree value, asset quality degree value and operating condition degree value of the financial institution at the current moment, thereby realizing an overall assessment of the financial health status of the financial institution at the current moment. According to the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments, the second risk judgment result of the financial institution at the current moment is obtained, and a second judgment is made on the risk of the financial institution at the current moment. According to the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment, the financial risk early warning result of the financial institution at the current moment is accurately obtained, thereby realizing the provision of timely financial risk early warnings for financial institutions to avoid or reduce potential losses.

[0068] Embodiment 2:

[0069] On the basis of Example 1, the financial risk early warning system based on big data, the first judgment module includes:

[0070] The acquisition submodule is used to obtain all profit parameters, all asset parameters and all operating parameters of the financial institution at the current moment, wherein all profit parameters include the amount of newly added liabilities, operating income and operating expenses, all asset parameters include the amount of newly added loans, the amount of newly added non-performing loans and the balance of loan loss reserves, and all operating parameters include the number of loan applications, the number of newly added non-performing loans and the number of newly added loans;

[0071] The first judgment submodule is used to obtain the first risk judgment result of the financial institution at the current moment based on all profit parameters, all asset parameters and all operation parameters of the financial institution at the current moment.

[0072] In this embodiment, the newly added liability amount is the newly added liability amount of the financial institution within a preset time period before the current moment.

[0073] In this embodiment, the operating income is the total income obtained by the financial institution through main business activities such as earning interest and providing services within a preset time period (one quarter) before the current moment.

[0074] In this embodiment, the operating expenses are various expenses incurred by the financial institution in carrying out daily operating activities within a preset time period (one quarter) before the current moment.

[0075] In this embodiment, the new loan amount is the total amount of new loans issued by the financial institution through loan approval within a preset time period (one quarter) before the current moment.

[0076] In this embodiment, the new amount of non-performing loans is the total amount of loans newly identified as non-performing loans (overdue) by the financial institution within a preset time period (one quarter) before the current moment.

[0077] In this embodiment, the loan loss reserve balance is the balance of the reserve set aside by the financial institution to compensate for possible loan losses on the balance sheet at the current moment.

[0078] In this embodiment, the number of loan applications is the total number of loan applications from customers of the financial institution within a preset time period (one quarter) before the current moment.

[0079] In this embodiment, the number of new non-performing loans is the total number of new non-performing loans (overdue) identified by the financial institution within a preset time period (one quarter) before the current moment.

[0080] In this embodiment, the number of new loans is the total number of new loans issued by the financial institution through loan approval within a preset time period (one quarter) before the current moment.

[0081] The beneficial effects of the above technology are: clarifying the specific parameter items of all profit parameters, all asset parameters and all operating parameters of the financial institution at the current moment, and obtaining the first risk judgment result of the financial institution at the current moment based on all profit parameters, all asset parameters and all operating parameters of the financial institution at the current moment.

[0082] Embodiment 3:

[0083] On the basis of Example 2, the financial risk early warning system based on big data, the first judgment submodule includes:

[0084] A judgment parameter extraction unit, used to use the newly added debt amount, newly added non-performing loan amount and newly added number of non-performing loans of the financial institution at the current moment as the judgment parameter of the financial institution at the current moment;

[0085] The first judgment unit is used to judge whether all judgment parameters of the financial institution at the current moment are less than the corresponding judgment parameter threshold. If so, the qualified is regarded as the first risk judgment result of the financial institution at the current moment; otherwise, the unqualified is regarded as the first risk judgment result of the financial institution at the current moment.

[0086] In this embodiment, the judgment parameter threshold is a pre-set threshold used to compare with each judgment parameter to obtain the first risk judgment result of the financial institution at the current moment, and each judgment parameter corresponds to a judgment parameter threshold separately.

[0087] The beneficial effects of the above technology are: based on all types of profit parameters, all types of asset parameters and all types of operating parameters of the financial institution at the current moment, the first risk judgment result of the financial institution at the current moment is obtained, and the first judgment on the risk of the financial institution at the current moment is made, which facilitates the subsequent accurate acquisition of the financial risk warning results of the financial institution at the current moment.

[0088] Embodiment 4:

[0089] On the basis of Example 1, in the financial risk early warning system based on big data, the processing module obtains the profitability value of the financial institution at the current moment based on all the class profitability parameters of the financial institution at the current moment, including:

[0090]

[0091] Among them, β is the profitability value of the financial institution at the current moment, z is the operating income of the financial institution at the current moment, c is the amount of new liabilities of the financial institution at the current moment, v is the operating expenses of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0092] The beneficial effect of the above technology is: based on all types of profit parameters of the financial institution at the current moment, the profitability level of the financial institution at the current moment is obtained, thereby quantifying the profitability of the financial institution at the current moment. This embodiment gives in detail a specific method for quantifying the profitability of the financial institution at the current moment.

[0093] Embodiment 5:

[0094] On the basis of Example 1, in the financial risk early warning system based on big data, the processing module obtains the asset quality value of the financial institution at the current moment based on all asset class parameters of the financial institution at the current moment, including:

[0095]

[0096] Among them, γ is the asset quality level of the financial institution at the current moment, h is the new loan amount of the financial institution at the current moment, g is the loan loss reserve balance of the financial institution at the current moment, b is the new non-performing loan amount of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0097] The beneficial effect of the above technology is: based on all asset-type parameters of the financial institution at the current moment, the asset quality level of the financial institution at the current moment is obtained, thereby quantifying the asset quality of the financial institution at the current moment. This embodiment gives in detail a specific method for quantifying the asset quality of the financial institution at the current moment.

[0098] Embodiment 6:

[0099] On the basis of Example 1, in the financial risk early warning system based on big data, the processing module obtains the method of the operating status value of the financial institution at the current moment based on all the operating parameters of the financial institution at the current moment, including:

[0100]

[0101] Among them, δ is the operating status value of the financial institution at the current moment, k is the number of loan applications of the financial institution at the current moment, w is the number of new loans of the financial institution at the current moment, r is the number of new non-performing loans of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0102] The beneficial effect of the above technology is: based on all types of operating parameters of the financial institution at the current moment, the operating status of the financial institution at the current moment is obtained, thereby quantifying the operating status of the financial institution at the current moment. This embodiment gives in detail a specific method for quantifying the operating status of the financial institution at the current moment.

[0103] Embodiment 7:

[0104] On the basis of Example 1, the financial risk early warning system based on big data, the second judgment module includes:

[0105] The evaluation calculation submodule is used to obtain the comprehensive financial evaluation value of the financial institution at the current moment based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, that is:

[0106]

[0107] Among them, θ is the comprehensive financial evaluation value of the financial institution at the current moment, β is the profitability value of the financial institution at the current moment, γ is the asset quality value of the financial institution at the current moment, and δ is the operating condition value of the financial institution at the current moment, (β,γ,δ) max is the maximum value among the profitability, asset quality and operating performance of the financial institution at the current moment, (β,γ,δ) min is the minimum value among the profitability, asset quality and operating performance of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718;

[0108] The second judgment submodule is used to obtain a second risk judgment result of the financial institution at the current moment based on the comprehensive financial evaluation value of the financial institution at the current moment and the comprehensive financial evaluation values ​​at all evaluation moments.

[0109] The beneficial effects of the above technology are: based on the profitability degree value, asset quality degree value and operating condition degree value of the financial institution at the current moment, the comprehensive financial assessment value of the financial institution at the current moment is obtained, thereby realizing an overall assessment of the financial health status of the financial institution at the current moment; based on the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments, the second risk judgment result of the financial institution at the current moment is obtained. This embodiment gives in detail a specific method for overall assessment of the financial health status of the financial institution at the current moment.

[0110] Embodiment 8:

[0111] On the basis of Example 7, the financial risk early warning system based on big data, the second judgment submodule includes:

[0112] An evaluation time determination unit, used to take a preset time period before the current time as the evaluation time period of the current time, and uniformly sample the evaluation time period of the current time based on a preset sampling number to obtain all sampling moments, and take all sampling moments as all evaluation moments of the current time;

[0113] An analysis unit, used to determine whether the comprehensive financial evaluation value of each evaluation moment of the financial institution at the current moment is greater than or equal to the comprehensive financial evaluation value of the financial institution at the current moment, and if so, the corresponding evaluation moment is regarded as the first evaluation moment of the current moment, otherwise, the corresponding moment is regarded as the second evaluation moment of the current moment;

[0114] The second judgment unit is used to take the quotient between the number of all first evaluation moments and the number of all second evaluation moments at the current moment as the judgment quotient value at the current moment, and judge whether the judgment quotient value at the current moment is greater than the preset judgment quotient value. If so, qualified is regarded as the second risk judgment result of the financial institution at the current moment; otherwise, unqualified is regarded as the second risk judgment result of the financial institution at the current moment.

[0115] In this embodiment, the preset time period is a time length preset to obtain the evaluation time period at the current moment. In this embodiment, the preset time period is one quarter.

[0116] In this embodiment, the preset number of samples is a number preset to perform uniform sampling of the evaluation time period at the current moment (the time length between each adjacent sampling moment is the same), for example, 30.

[0117] In this embodiment, the preset judgment quotient value is a quotient value preset to obtain the second risk judgment result of the financial institution at the current moment.

[0118] The beneficial effects of the above technology are: it clarifies the method for determining all evaluation moments at the current moment, and then obtains the first evaluation moment at the current moment and the second evaluation moment at the current moment based on the comprehensive financial evaluation value of the financial institution at the current moment and the comprehensive financial evaluation value of all evaluation moments, and finally obtains the second risk judgment result of the financial institution at the current moment based on the first evaluation moment at the current moment and the second evaluation moment at the current moment, and makes a second judgment on the risk of the financial institution at the current moment, which facilitates the subsequent accurate acquisition of the financial risk warning results of the financial institution at the current moment.

[0119] Embodiment 9:

[0120] Based on Example 1, the financial risk early warning system based on big data, the early warning module, includes:

[0121] The early warning analysis submodule is used to treat the need for alarm as the financial early warning analysis result at the current moment when the first risk judgment result or the second risk judgment result of the financial institution at the current moment is unqualified, and treat the need for alarm as the financial early warning analysis result at the current moment when both the first risk judgment result and the second risk judgment result of the financial institution at the current moment are qualified;

[0122] The early warning control submodule is used to control the preset alarm device to sound an alarm if the financial early warning analysis result at the current moment indicates that an alarm is required.

[0123] In this embodiment, the financial early warning analysis result at the current moment is a judgment result on whether the financial institution needs to issue a risk alarm at the current moment, which is obtained based on the first risk judgment result and the second risk judgment result of the financial institution at the current moment, wherein the financial early warning analysis result at the current moment includes whether an alarm is needed or not.

[0124] The beneficial effects of the above technology are: based on the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment, the financial risk warning result of the financial institution at the current moment is accurately obtained, thereby providing timely financial risk warnings for the financial institution and avoiding or reducing potential losses.

[0125] Embodiment 10:

[0126] The present invention provides a financial risk early warning method based on big data, which is applied to any one of the financial risk early warning systems based on big data in embodiments 1 to 9, with reference to Figure 2 ,include:

[0127] S1: Based on all profit parameters, asset parameters and operation parameters of the financial institution at the current moment, obtain the first risk judgment result of the financial institution at the current moment;

[0128] S2: Based on all profit parameters of the financial institution at the current moment, obtain the profitability value of the financial institution at the current moment; based on all asset parameters of the financial institution at the current moment, obtain the asset quality value of the financial institution at the current moment; based on all operating parameters of the financial institution at the current moment, obtain the operating condition value of the financial institution at the current moment;

[0129] S3: Based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, obtain the comprehensive financial evaluation value of the financial institution at the current moment, and based on the comprehensive financial evaluation value of the financial institution at the current moment and the comprehensive financial evaluation values ​​at all evaluation moments, obtain the second risk judgment result of the financial institution at the current moment;

[0130] S4: Based on the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment, obtain the financial risk warning result of the financial institution at the current moment.

[0131] The beneficial effects of the above technology are as follows: based on all profit parameters, asset parameters and operating parameters of the financial institution at the current moment, the first risk judgment result of the financial institution at the current moment is obtained, and the risk of the financial institution at the current moment is judged for the first time; based on all profit parameters of the financial institution at the current moment, the profitability degree value of the financial institution at the current moment is obtained, and the profitability of the financial institution at the current moment is quantified; based on all asset parameters of the financial institution at the current moment, the asset quality degree value of the financial institution at the current moment is obtained, and the asset quality of the financial institution at the current moment is quantified; based on all operating parameters of the financial institution at the current moment, the operating condition degree value of the financial institution at the current moment is obtained, and the financial institution at the current moment is quantified. The operating conditions at the current moment are quantified, and the comprehensive financial assessment value of the financial institution at the current moment is obtained according to the profitability degree value, asset quality degree value and operating condition degree value of the financial institution at the current moment, thereby realizing an overall assessment of the financial health status of the financial institution at the current moment. According to the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments, the second risk judgment result of the financial institution at the current moment is obtained, and a second judgment is made on the risk of the financial institution at the current moment. According to the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment, the financial risk early warning result of the financial institution at the current moment is accurately obtained, thereby realizing the provision of timely financial risk early warnings for financial institutions to avoid or reduce potential losses.

[0132] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention, and the present invention is also intended to include these changes and modifications.

Claims

1. A financial risk early warning system based on big data, characterized in that: include: A first judgment module, used to obtain a first risk judgment result of the financial institution at the current moment based on all profit parameters, all asset parameters and all operation parameters of the financial institution at the current moment; A processing module, used to obtain the profitability degree value of the financial institution at the current moment based on all the profit-like parameters of the financial institution at the current moment, obtain the asset quality degree value of the financial institution at the current moment based on all the asset-like parameters of the financial institution at the current moment, and obtain the operating condition degree value of the financial institution at the current moment based on all the operating parameters of the financial institution at the current moment; A second judgment module is used to obtain a comprehensive financial assessment value of the financial institution at the current moment based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, and obtain a second risk judgment result of the financial institution at the current moment based on the comprehensive financial assessment value of the financial institution at the current moment and the comprehensive financial assessment values ​​at all assessment moments; An early warning module, used to obtain a financial risk early warning result of the financial institution at the current moment based on a first risk judgment result of the financial institution at the current moment and a second risk judgment result of the financial institution at the current moment; The second judgment module includes a second judgment submodule, including: An evaluation time determination unit, used to take a preset time period before the current time as the evaluation time period of the current time, and uniformly sample the evaluation time period of the current time based on a preset sampling number to obtain all sampling moments, and take all sampling moments as all evaluation moments of the current time; An analysis unit, used to determine whether the comprehensive financial evaluation value of each evaluation moment of the financial institution at the current moment is greater than or equal to the comprehensive financial evaluation value of the financial institution at the current moment, and if so, the corresponding evaluation moment is regarded as the first evaluation moment of the current moment, otherwise, the corresponding moment is regarded as the second evaluation moment of the current moment; The second judgment unit is used to take the quotient between the number of all first evaluation moments and the number of all second evaluation moments at the current moment as the judgment quotient value at the current moment, and judge whether the judgment quotient value at the current moment is greater than the preset judgment quotient value. If so, qualified is regarded as the second risk judgment result of the financial institution at the current moment; otherwise, unqualified is regarded as the second risk judgment result of the financial institution at the current moment.

2. The financial risk early warning system based on big data according to claim 1 is characterized in that: The first judgment module includes: The acquisition submodule is used to obtain all profit parameters, all asset parameters and all operating parameters of the financial institution at the current moment, wherein all profit parameters include the amount of newly added liabilities, operating income and operating expenses, all asset parameters include the amount of newly added loans, the amount of newly added non-performing loans and the balance of loan loss reserves, and all operating parameters include the number of loan applications, the number of newly added non-performing loans and the number of newly added loans; The first judgment submodule is used to obtain the first risk judgment result of the financial institution at the current moment based on all profit parameters, all asset parameters and all operation parameters of the financial institution at the current moment.

3. The financial risk early warning system based on big data according to claim 2 is characterized in that: The first judgment submodule includes: A judgment parameter extraction unit, used to use the newly added debt amount, newly added non-performing loan amount and newly added number of non-performing loans of the financial institution at the current moment as the judgment parameter of the financial institution at the current moment; The first judgment unit is used to judge whether all judgment parameters of the financial institution at the current moment are less than the corresponding judgment parameter threshold. If so, the qualified is regarded as the first risk judgment result of the financial institution at the current moment; otherwise, the unqualified is regarded as the first risk judgment result of the financial institution at the current moment.

4. The financial risk early warning system based on big data according to claim 1 is characterized in that: The method for the processing module to obtain the profitability value of the financial institution at the current moment based on all the class profitability parameters of the financial institution at the current moment includes: Among them, β is the profitability value of the financial institution at the current moment, z is the operating income of the financial institution at the current moment, c is the amount of new liabilities of the financial institution at the current moment, v is the operating expenses of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.

718.

5. The financial risk early warning system based on big data according to claim 1 is characterized in that: The method for the processing module to obtain the asset quality value of the financial institution at the current moment based on all asset class parameters of the financial institution at the current moment includes: Among them, γ is the asset quality level of the financial institution at the current moment, h is the new loan amount of the financial institution at the current moment, g is the loan loss reserve balance of the financial institution at the current moment, b is the new non-performing loan amount of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.

718.

6. The financial risk early warning system based on big data according to claim 1 is characterized in that: The method for the processing module to obtain the operation status value of the financial institution at the current moment based on all the class operation parameters of the financial institution at the current moment includes: Among them, δ is the operating status value of the financial institution at the current moment, k is the number of loan applications of the financial institution at the current moment, w is the number of new loans of the financial institution at the current moment, r is the number of new non-performing loans of the financial institution at the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.

718.

7. The financial risk early warning system based on big data according to claim 1 is characterized in that: The second judgment module includes: The evaluation calculation submodule is used to obtain the comprehensive financial evaluation value of the financial institution at the current moment based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, that is: Among them, θ is the comprehensive financial evaluation value of the financial institution at the current moment, β is the profitability value of the financial institution at the current moment, γ is the asset quality value of the financial institution at the current moment, and δ is the operating condition value of the financial institution at the current moment. ( β,γ,δ )max It is the maximum value among the profitability, asset quality and operating performance of the financial institution at the current moment. ( β,γ,δ )min It is the minimum value among the profitability, asset quality and operating status of the financial institution at the current moment. ln is the natural logarithm, and the value of the natural constant e is 2.

718.

8. The financial risk early warning system based on big data according to claim 1 is characterized in that: Early warning module, including: The early warning analysis submodule is used to treat the need for alarm as the financial early warning analysis result at the current moment when the first risk judgment result or the second risk judgment result of the financial institution at the current moment is unqualified, and treat the need for alarm as the financial early warning analysis result at the current moment when both the first risk judgment result and the second risk judgment result of the financial institution at the current moment are qualified; The early warning control submodule is used to control the preset alarm device to sound an alarm if the financial early warning analysis result at the current moment indicates that an alarm is required.

9. A financial risk early warning method based on big data, characterized in that: A financial risk early warning system based on big data as described in any one of claims 1 to 8, comprising: S1: Based on all profit parameters, all asset parameters and all operation parameters of the financial institution at the current moment, obtain the first risk judgment result of the financial institution at the current moment; S2: Based on all profit parameters of the financial institution at the current moment, obtain the profitability value of the financial institution at the current moment; based on all asset parameters of the financial institution at the current moment, obtain the asset quality value of the financial institution at the current moment; based on all operating parameters of the financial institution at the current moment, obtain the operating condition value of the financial institution at the current moment; S3: Based on the profitability degree value, asset quality degree value and operation degree value of the financial institution at the current moment, obtain the comprehensive financial evaluation value of the financial institution at the current moment, and based on the comprehensive financial evaluation value of the financial institution at the current moment and the comprehensive financial evaluation values ​​at all evaluation moments, obtain the second risk judgment result of the financial institution at the current moment; S4: Based on the first risk judgment result of the financial institution at the current moment and the second risk judgment result of the financial institution at the current moment, obtain the financial risk warning result of the financial institution at the current moment.

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