Quality Assurance Strategies in Developing High-Performance Financial Technology Solutions

Аннотация

Ensuring that financial technology solutions are effective, safe, and comply with regulations is necessary in the changing technology field. The research introduces a new Quality Assurance framework that ensures that FinTech systems follow strict rules, process transactions instantly, and have the most secure possible systems. Using up-to- date automated testing, optimization techniques, and CI/CD practices, the approach boosts the system’s reliability, scalability, and quick response. Research shows that using this approach boosts defect detection results, speeds up development, and reduces risks, setting a new high standard for QA in FinTech. This study provides useful information for both experts and academics working on improving software quality and system dependability in high-stakes finance. KEYWORDS Quality Assurance, Fintech, Automated Testing, CI/CD, Improving Performance 1. INTRODUCTION Over the past few years, the world’s financial industry has been shaped by the use of FinTech solutions. Thanks to these new developments, banking, investment, insurance and payment systems operate much faster, can be accessed more easily and are often more inexpensive. FinTech includes features like mobile banking, sharing your money with other individuals, calculating investment plans and safe and fair transactions with the help of blockchain technology. Even so, these systems are convenient and scalable, though they introduce certain software-related issues. Regular software does not face the same demands for accuracy, quick response and security as those expected from FinTech which sets FinTech appapart. If these systems have minor issues with speed, they may cause financial damage, legal troubles or make customers distrustful. Cutting-edge tools such as blockchain, AI and DeFi have increased difficulties faced by people in QA. Since blockchain technology includes distributed settings and protocols for agreement, it requires new approaches to testing for unchangeable and intact data. As there is no central authority on DeFi, quality assurance plans need to cheque smart contracts and defend them from hacks in unstable situations. Furthermore, AI is now included in FinTech systems to check for potential fraud, assess credit scores and analyze users which means QA should ensure model transparency, spot differences in models and identify biases. Because of these advancing tech systems, the QA team must now ensure responsiveness, reliability and that the systems help meet regulations in complex https://www.academicpublishers.org/journals/index.php/ijdsml 323 AMERICAN ACADEMIC PUBLISHER settings. Still, the current set of QA strategies in FinTech does not always reach the necessary outcomes when handling unique challenges in the field. Most standard QA frameworks do not address the challenges of high-speed and fully complying with regulations. For example, the availability of services in high-traffic conditions and extremely fast response for real-time actions are yet to be widely tested in regular systems. Also, including global financial regulations such as GDPR, PSD2 and PCI DSS in testing is not always done without issues. Due to this disconnect, platforms may encounter significant problems such as fines or cyber attacks. Chen and Singh (2019) points out that many FinTech applications stumble when they have to process information within milliseconds and be error- resistant. The study aims to overcome these issues by suggesting a new QA framework that focuses on high-performance systems in FinTech. The primary focus is on strategies that meet all needs, are dependable, scalable and stay in compliance with rules in practical use. Researchers look into ways of using AI for anomaly detection, blockchain for auditing and chaos engineering in the testing process. Operational strategies are shaped to track progress in real time and ensure they comply with updates in rules and laws globally. This paper has successfully created and proved a question-answering framework tailor-made for FinTech systems. This model takes into account technology, how things are run and any associated regulations, unlike standard models. The process involves incorporating test automation, stressing performance, checking security and ensuring compliance across areas like credibility, transactions and the system working smoothly, all tied to the most important outcomes for a business. In addition, the study provides a comparison of FinTech case studies to outline that this framework is more reliable, efficient and cost-effective than previous QA systems. 2. Systematic Literature Review For years, Software Quality Assurance (SQA) has worked according to plan-driven systems, where quality was evaluated once formal testing was done after the development cycle ended. Historically, quality assurance was more about finishing a process rather than staying involved throughout. Over the years, there have been many changes to this perspective. Because of