Besion Kolli is a Senior Consultant in Financial and Risk Analysis with expertise in Business Analysis, Credit and Market Risk, and Project Management for the implementation of risk software solutions. He specializes in extracting, analyzing, and reporting financial data from diverse accounting and information systems and in developing advanced risk models. Besion’s technical proficiency includes SQL, Excel, and Python, which he leverages to provide comprehensive risk assessments. Additionally, he has extensive knowledge of Risk Authority Software, which is used for Regulatory Risk Reporting and Calculation. His project management experience includes overseeing the implementation of risk software, ensuring that solutions are delivered efficiently, within scope, and compliant with regulatory requirements.
Introduction
Functional testing is a critical process in software development ensuring that applications perform according to specified requirements. In financial risk management, where regulatory compliance and data integrity are paramount, functional testing is essential to verify the accuracy and reliability of risk calculation tools. The Finalyse Risk Data Analyzer enhances functional testing by automating risk calculations validations, ensuring data consistency, and supporting compliance with industry standards. It provides value to both functional and technical users by simplifying the detection and resolution of data anomalies. Consequently, Finalyse risk data analyzer stands out as a key asset to boost the efficiency of any regulatory reporting implementation projects and continues to deliver value in business-as-usual stage, strengthening stability and regression testing of the solution it is applied to.
This article describes best practices in functional testing, supported by practical examples and demonstrates how Finalyse Risk Data Analyzer aligns with quality standards such as ISO 25010 (Software Quality Model) and ISO 9001 (Quality Management Systems), highlighting its value in ensuring financial data accuracy.
Best Practices for Functional Testing with Finalyse Risk Data Analyzer
Strategic Test Case Selection
Why it matters
Selecting the right test cases is crucial for effective functional testing. Best practices recommend testing critical functionalities, high-risk areas, and compliance requirements. ISO 29119 (Software Testing Standard) emphasizes risk-based testing, which prioritizes test cases based on their impact.
How Finalyse Risk Data Analyzer supports this
- Ensures Risk-Weighted Asset (RWA) calculations are validated across multiple methodologies (SA, F-IRB, A-IRB).
- Identifies anomalies in risk models, reducing potential compliance breaches.
- Generates automated reports to validate consistency between input data and final regulatory reports.
Data-Driven Testing for Financial Accuracy
Why it matters
Data-driven testing (DDT) involves testing applications with multiple input datasets to verifyperformance under different conditions. This approach aligns with ISO 25010, which emphasises functional suitability and accuracy.
How Finalyse Risk Data Analyzer supports this
- Leverages real-world financial datasets to test various risk scenarios.
- Uses automated processes, easily configurable by non-technical users, to compare results across different portfolios and stress-testing scenarios.
- Enhances data integrity by ensuring consistency between historical and current risk calculations through comprehensive comparison dashboards.
Intelligent TestiAutomation for Regulatory Compliance
Why it matters
Test automation improves efficiency and reduces human error. ISO 9001 emphasizes automation in quality management to maintain consistency and reliability.
How Finalyse Risk Data Analyzer supports this
- Automates large-scale financial dataset validation to detect inconsistencies based on predefined Data Quality checks that can be set up by the user
- Enables regression testing to verify that system updates do not introduce errors.
- Enables automated impact analysis to assess how data changes affect risk calculations.
Early Testing Initiation (Shift -Left Testing)
Why it matters
Shift -left testing, a best practice in Agile and DevOps methodologies, promotes early defect detection, reducing costs and improving software quality.
How Finalyse Risk Data Analyzer supports this
- Performs pre-emptive data quality checks before executing complex calculations.
- Allows early-stage validation of financial data to mitigate regulatory non-compliance risks.
- Reduces financial reporting errors by detecting inconsistencies before final assessments.
- Check Regulatory Reports like COREP or FINREP against EBA Validation Rules before submission
Combining Scripted and Exploratory Testing for Risk Assessment
Why it matters
While scripted tests ensure standard functionalities work as expected, exploratory testing helps discover unexpected system behaviors. ISO 29119 recognizes both approaches for effective functional validation.
How Finalyse Risk Data Analyzer supports this
- Uses automated procedure to validate regulatory compliance requirements.
- Allows exploratory testing to identify edge cases and anomalies in risk-weight calculations.
- Simulates market fluctuations to verify how the system responds to financial stress scenarios.
Reusable Test Cases for Long-Term Efficiency
Why it matters
Reusable test cases reduce maintenance effort and enhance consistency across testing cycles. IEEE 829 (Test Documentation Standard) recommends structured test documentation to improve test case reusability.
How Finalyse Risk Data Analyzer Supports This
- Creates standardized test scripts that apply across multiple risk models and datasets.
- Adapts test cases to accommodate regulatory updates without rewriting them from scratch.
- Optimises efficiency by enabling automated batch testing for different financial scenarios.
Specification by Example for Business Alignment
Why it matters
Specification by Example (SBE) ensures test cases are based on real-world financial scenarios. This methodology aligns testing with business goals, promoting clarity in requirements and reducing misinterpretations.
How Finalyse Risk Data Analyzer supports this
- Uses realistic financial case studies to validate risk calculations.
- Ensures that test cases align with regulatory standards.
- Facilitates collaboration between financial analysts and testers to refine functional testing parameters.
Continuous Monitoring and Test Optimization
Why it matters
Functional testing should be a continuous process to adapt to evolving risks and regulatory requirements. ISO 20000 (IT Service Management Standard) advocates for continuous monitoring and improvement.
How Finalyse Risk Data Analyzer Supports This
- Integrates real-time monitoring to detect and resolve issues proactively.
- Uses AI-driven insights to optimize test coverage and improve risk analytics.
- Enhances predictive risk assessment through automated anomaly detection in financial datasets.
Conclusion
Whether during a project implementation testing phase or a reporting cycle, integrating these best practices with Finalyse Risk Data Analyzer enables financial institutions to strengthen their functional testing frameworks, ensuring accurate risk assessments, regulatory compliance, and operational efficiency.
By aligning with industry standards like ISO 25010, ISO 29119, and Regulatory requirements, the Finalyse Risk Data Analyzer provides a robust solution for financial risk management. Its capabilities in risk calculation validation, data integrity checks, and compliance reporting ensure error-free operations, ultimately enhancing decision-making for financial institutions.
It also helps to automate and speed up testing, focusing it on the priority area that has the most impact in guaranteeing error free reports.
Frequently Asked Questions
The tool automates validation of risk calculations, ensures data consistency, and supports compliance with regulatory requirements (e.g., COREP, FINREP, EBA rules). This reduces errors, speeds up testing, and improves accuracy in financial reporting
Yes. The solution is designed with configurable automated processes and comparison dashboards, making it accessible to both functional and technical users. Financial analysts, for example, can run data quality checks and explore scenarios without deep technical expertise
By performing pre-emptive data quality checks and validating financial data before complex calculations, the analyzer helps detect issues earlier in the process. This minimizes costly late-stage corrections and ensures higher quality regulatory submissions
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