Quality risk assessment method and system for bank software

Abstract

The invention discloses a quality risk assessment method and system for bank software, and the method comprises the steps: collecting the data of the bank software, and obtaining the collected data; key risk indexes are extracted from the collected data; a machine learning model is used for training and predicting bank software quality risks, and a prediction result is obtained; and providing targeted risk mitigation measure suggestions according to the prediction result. And a more efficient and accurate bank software quality risk assessment means is provided. The quality risk of bank software can be automatically and comprehensively identified and evaluated, the efficiency and accuracy of risk evaluation are improved, and the security and stability of bank business are ensured.

Description

Technical Field

[0001] The present invention relates to the field of quality risk assessment of banking software, and in particular to a quality risk assessment method and system for banking software. Background Art

[0002] In the context of the rapid development of information technology, banking business is increasingly dependent on the stable operation of software systems. In order to ensure the stability and security of banking software systems, strict risk assessment and management of software quality are required. At present, the quality risk assessment of banking software is mostly done manually or semi-automatically, which is inefficient and prone to missing risk points, and cannot fully and timely reflect the true status of software quality.

[0003] At present, the quality risk assessment of banking software is mostly carried out manually or semi-automatically, which is inefficient and prone to missing risk points, and cannot fully and timely reflect the true status of software quality. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a method and system for quality risk assessment of banking software that overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for assessing the quality risk of banking software is provided, the method comprising:

[0006] Collect data from banking software and obtain collected data;

[0007] Extracting key risk indicators based on the collected data;

[0008] Use machine learning models to train and predict banking software quality risks and obtain prediction results;

[0009] Provide targeted risk mitigation measures based on the forecast results.

[0010] Optionally, the collecting data from the bank software to obtain the collected data specifically includes:

[0011] Collect banking software's operating data, log information and code change records;

[0012] Clean and organize historical defect data, code change records, and security audit reports.

[0013] Optionally, extracting key risk indicators from the collected data specifically includes:

[0014] Analyze the collected data and identify potential quality risks;

[0015] Extract key risk indicators from the data based on preset rules and algorithms.

[0016] Optionally, the potential quality risks include: code defects, performance bottlenecks and security vulnerabilities.

[0017] Optionally, the using of a machine learning model to train and predict banking software quality risks to obtain prediction results specifically includes:

[0018] Based on key risk indicators, preset algorithms and models are used to quantitatively assess risks and determine risk levels.

[0019] Optionally, providing targeted risk mitigation measures suggestions based on the prediction results specifically includes:

[0020] Generate detailed risk assessment reports;

[0021] And propose risk response strategies based on the risk assessment report.

[0022] Optionally, the risk assessment report includes: risk description, risk level, impact analysis and recommended response measures.

[0023] Optionally, the risk response strategy specifically includes: risk mitigation measures, prevention plans and emergency plans.

[0024] The present invention also provides a quality risk assessment system for banking software, which uses the above-mentioned quality risk assessment method for banking software. The risk assessment system specifically includes:

[0025] Data collection module, used to collect data from banking software and obtain collected data;

[0026] A risk factor analysis module, used to extract key risk indicators from the collected data;

[0027] Model training and prediction module, used to use machine learning models to train and predict banking software quality risks and obtain prediction results;

[0028] The risk response suggestion module is used to provide targeted risk mitigation measures suggestions based on the prediction results.

[0029] The present invention provides a method and system for assessing the quality risk of banking software. The risk assessment method includes: collecting data of banking software to obtain the collected data; extracting key risk indicators from the collected data; using a machine learning model to train and predict the quality risk of banking software to obtain a prediction result; and providing targeted risk mitigation measures based on the prediction result. A more efficient and accurate means for assessing the quality risk of banking software is provided. The method can automatically and comprehensively identify and assess the quality risk of banking software, improve the efficiency and accuracy of risk assessment, and thus ensure the safety and stability of banking business.

[0030] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0032] Figure 1 A timing diagram of a quality risk assessment method for banking software provided by an embodiment of the present invention;

[0033] Figure 2 A flow chart of a quality risk assessment system for banking software provided by an embodiment of the present invention;

[0034] Figure 3 A composition diagram of a quality risk assessment system for banking software provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0036] The terms "comprises" and "having" and any variations thereof in the description embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.

[0037] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0038] like Figure 1 As shown, a quality risk assessment method for banking software includes: collecting data of banking software to obtain collected data; extracting key risk indicators based on the collected data; using a machine learning model to train and predict banking software quality risks to obtain prediction results; and providing targeted risk mitigation measures based on the prediction results.

[0039] like Figure 2 and Figure 3 As shown, a quality risk assessment system for banking software includes:

[0040] Data preprocessing module: cleans and organizes historical defect data, code change records, security audit reports, etc.

[0041] Risk factor analysis module: extracts key risk indicators from data based on preset rules and algorithms.

[0042] Model training and prediction module: Use machine learning models (such as decision trees and neural networks) to train and predict software quality risks.

[0043] Risk response recommendation module: Provides targeted risk mitigation measures based on the prediction results.

[0044] Data collection module: responsible for collecting banking software's operating data, log information, code change records, etc. for subsequent analysis.

[0045] Risk identification module: Identify potential quality risks such as code defects, performance bottlenecks, security vulnerabilities, etc. by analyzing the collected data.

[0046] Risk Analysis Module: Conduct in-depth analysis of identified risks to assess their severity, probability of occurrence and scope of impact.

[0047] Risk quantification module: Based on the risk analysis results, the preset algorithms and models are used to conduct quantitative risk assessment and determine the risk level.

[0048] Risk Report Module: Generates a detailed risk assessment report including risk description, risk level, impact analysis and recommended response measures.

[0049] Risk Response Module: Propose risk response strategies, including risk mitigation measures, prevention plans and emergency plans.

[0050] Risk monitoring module: continuously monitors the operating status of banking software, updates risk assessments in real time, and adjusts response strategies.

[0051] Requirements analysis and definition

[0052] Goal setting: Clarify the goals of software quality assessment, such as improving security, stability, etc.

[0053] Risk Type: Define the different types of risks you might encounter, such as functional risk, performance risk, security risk.

[0054] Data acquisition module, including:

[0055] Static code analysis: using tools to check for potential problems in source code;

[0056] Dynamic testing: simulate user operations for automated testing;

[0057] User feedback collection: Collect usage feedback information from end users.

[0058] Risk analysis module, including:

[0059] Rule engine: Establish a set of rules to determine which items are high-risk;

[0060] Machine learning models: Use historical data to train models to predict future risk trends;

[0061] Expert system: Integrates the knowledge of domain experts to assist decision making.

[0062] Report generation module, including:

[0063] Automatic reporting: Automatically generate detailed evaluation reports based on analysis results;

[0064] Visual presentation: Provide charts and other forms to intuitively display the evaluation results.

[0065] Testing and verification, including: unit testing: independent functional testing of each module; integration testing: ensuring that there are no problems when all modules work together; stress testing: simulating software performance under extreme conditions.

[0066] Continuous improvement, including: Iterative optimization: continuously adjust and optimize algorithms based on problems found in actual use; Update strategy: regularly update risk assessment standards to adapt to new threat environments.

[0067] Security and compliance, including: Data protection: ensuring that all processed data complies with relevant laws and regulations; Privacy protection: protecting user privacy from infringement.

[0068] Implementation plan, including: Phase division: divide the entire project into several manageable phases; Timetable: develop a detailed timetable to track progress.

[0069] Training and support, including: User training: providing necessary usage training for relevant personnel; Technical support: establishing an effective technical support system to solve problems arising during use.

[0070] Monitoring and maintenance, including: Continuous monitoring: Regularly checking the status of the system to ensure it is functioning properly.

[0071] Version update: New versions are released as needed to fix known issues or add new features.

[0072] 1. Data collection layer:

[0073] Static code analysis tool: SonarQube is used to scan code quality.

[0074] Dynamic testing tools: Selenium is used for UI automation testing.

[0075] Log collector: ELK Stack (Elasticsearch, Logstash, Kibana) is used to collect runtime log information.

[0076] 2. Risk analysis layer:

[0077] Rules Engine: Use the Drools rules engine to define and execute business rules.

[0078] Machine Learning Model: Use Python’s Scikit-learn library to train a classification model to identify potential risks.

[0079] Expert systems: integrating expert knowledge in the financial field.

[0080] 3. Report generation layer:

[0081] Report Templates: Generate detailed assessment reports using JasperReports.

[0082] Dashboards: Use Tableau or PowerBl to create visual risk monitoring dashboards.

[0083] 4. User interface layer:

[0084] Web Interface: The front end uses React.js to build a user-friendly interface.

[0085] The specific method flow is as follows Figure 3 As shown, including:

[0086] 1. Data Collection

[0087] Static code analysis: Use SonarQube to scan the code base to find potential security vulnerabilities, coding standards issues, etc.

[0088] Dynamic testing: Use Selenium to simulate user behavior and record the performance of the system under various conditions.

[0089] Log collection: Collect all log data generated in the production environment through ELK Stack.

[0090] 2. Risk Analysis

[0091] Rules Engine: Automatically identifies non-compliant operations or abnormal behaviors based on preset rules.

[0092] Machine learning: Use historical data to train models to predict future risk trends.

[0093] Expert system: Integrate the opinions and suggestions of industry experts to enhance the system's judgment ability.

[0094] 3. Report Generation

[0095] Automated reporting: When the risk analysis is completed, a detailed report document is automatically generated.

[0096] Dashboard display: Real-time display of key indicators and risk levels, allowing decision makers to quickly understand the current status.

[0097] 4. User Interaction

[0098] Permission management: Set different levels of access permissions to ensure the security of sensitive information.

[0099] User Feedback: Provide feedback channels so that users can report problems or suggestions.

[0100] The present invention realizes the automated assessment of bank software quality risks and improves the assessment efficiency; comprehensively identifies and analyzes risks to ensure the systematicness and accuracy of the assessment; monitors and dynamically adjusts risk response measures in real time to effectively reduce the impact of risks; and provides decision support for the continuous improvement and optimization of bank software.

[0101] Beneficial effects: Automated risk assessment, reducing labor costs; improving assessment accuracy, reducing software failure rate; dynamic monitoring, real-time warning, enhancing software security and stability.

[0102] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A quality risk assessment method for banking software, characterized in that: The risk assessment method includes: Collect data from banking software and obtain collected data; Extracting key risk indicators based on the collected data; Use machine learning models to train and predict banking software quality risks and obtain prediction results; Provide targeted risk mitigation measures based on the forecast results.

2. A quality risk assessment method for banking software according to claim 1, characterized in that: The collecting of bank software data and obtaining the collected data specifically include: Collect banking software's operating data, log information and code change records; Clean and organize historical defect data, code change records, and security audit reports.

3. A quality risk assessment method for banking software according to claim 1, characterized in that: The extracting of key risk indicators from the collected data specifically includes: Analyze the collected data and identify potential quality risks; Extract key risk indicators from the data based on preset rules and algorithms.

4. A quality risk assessment method for banking software according to claim 3, characterized in that: The potential quality risks include: code defects, performance bottlenecks and security vulnerabilities.

5. A quality risk assessment method for banking software according to claim 1, characterized in that: The use of machine learning models to train and predict banking software quality risks and obtain prediction results specifically include: Based on key risk indicators, preset algorithms and models are used to quantitatively assess risks and determine risk levels.

6. A quality risk assessment method for banking software according to claim 1, characterized in that: The targeted risk mitigation measures suggestions provided based on the prediction results specifically include: Generate detailed risk assessment reports; And propose risk response strategies based on the risk assessment report.

7. A quality risk assessment method for banking software according to claim 6, characterized in that: The risk assessment report includes: risk description, risk level, impact analysis and recommended response measures.

8. A method for quality risk assessment of banking software according to claim 6, characterized in that: The risk response strategies specifically include: risk mitigation measures, prevention plans and emergency plans.

9. A banking software quality risk assessment system, using a banking software quality risk assessment method according to any one of claims 1 to 8, characterized in that: The risk assessment system specifically includes: Data collection module, used to collect data from banking software and obtain collected data; A risk factor analysis module, used to extract key risk indicators from the collected data; Model training and prediction module, used to use machine learning models to train and predict banking software quality risks and obtain prediction results; The risk response suggestion module is used to provide targeted risk mitigation measures suggestions based on the prediction results.

Images