The financial sector is undergoing one of its most transformative periods, driven by digitalization, artificial intelligence, and evolving regulatory landscapes. Banks are increasingly expected to offer seamless, secure, and compliant digital experiences while maintaining operational efficiency.
As competition intensifies, banks must adopt smarter approaches to software quality assurance (QA). A reliable QA strategy ensures that banking applications meet customer expectations while complying with strict regulatory requirements.
Poorly tested systems can result in security breaches, compliance failures, and operational disruptions, all of which can damage a bank's reputation and bottom line.
This article highlights key trends in QA for banking for 2025, providing insights that help C-level executives make informed decisions about their company's testing strategies.
AI-powered QA is transforming software reliability
The banking sector has long relied on automation for processing transactions, detecting fraud, and optimizing risk assessment. Now, artificial intelligence is redefining QA processes by making software testing more proactive and predictive. Traditional testing methods struggle to keep up with the complexity of modern banking applications, where real-time data processing and seamless user experiences are critical.
Why banks need AI-driven QA:
- Faster bug detection – AI models can analyze vast amounts of test data, identifying patterns that predict failures before they occur.
- Enhanced security – AI-powered testing can quickly assess vulnerabilities and help banks comply with cybersecurity regulations.
- Optimized test coverage – AI-driven test automation improves efficiency by ensuring that critical customer journeys are always tested.
Despite its advantages, AI in QA is not a silver bullet. Banks must ensure that AI models operate in accordance with legal and security requirements while maintaining transparency and accuracy. When applied correctly, AI-powered QA can help find and resolve issues before they affect users.
Regulatory compliance and security testing are critical priorities
The regulatory landscape in banking is constantly evolving. As digital financial services expand, so do compliance requirements related to data privacy, fraud prevention, and cybersecurity. Inadequate testing can expose banks to legal risks, regulatory fines, and reputational damage.
Key regulatory challenges for QA in banking:
- Data security and consent management – Banks must ensure that customer data is processed securely and in compliance with privacy laws. QA must verify that customer consent mechanisms are implemented correctly.
- API and third-party integrations – With open banking on the rise, banks rely on external services and APIs, which need thorough security testing to prevent breaches.
- Vulnerability assessment – Cybersecurity threats are constantly evolving, making security testing an ongoing priority.
Given these challenges, banks must invest in QA suites that provide comprehensive security testing while ensuring that systems remain compliant. Automated compliance checks help validate that software changes moved into production do not introduce regulatory risks.
Test automation is driving efficiency and stability
Manual testing is no longer sufficient for large-scale banking applications. As financial institutions shift to digital-first models, they must embrace test automation to keep up with rapid development cycles. Automated QA enhances software speed, stability, and reliability while reducing the risk of human error.
According to Accenture, a major bank's new end-to-end testing solution enabled it to automate 84% of its testing processes, significantly reducing time to market.
The benefits of test automation for banks include:
- Faster time to market – Automation enables continuous testing, allowing banks to deploy software updates more quickly.
- Consistent quality – Automated tests eliminate variability, ensuring that all critical functions are tested in every release.
- Reduced operational risk – Automated testing detects bugs before they impact end-users, improving reliability.
However, not all automation solutions are built for the complexity of banking systems. Many tools generate false positives, causing delays in software releases. This is where TestResults stands out. Our automation technology reduces false positives by 99%, providing highly reliable test results.
In addition to functionality testing, marketing communications compliance is another critical area for automated QA. Banks must ensure that promotional messages and disclosures meet regulatory standards. Test automation can verify that communications across different channels adhere to legal requirements.
QA strategies must evolve for digital-first banking and embedded finance
As financial ecosystems become more interconnected, QA must adapt to test applications that extend beyond traditional banking platforms. Embedded finance, Banking-as-a-Service (BaaS), and digital-only banks are creating new testing challenges that require a more comprehensive QA approach.
New challenges in QA for digital-first banking:
- Testing third-party integrations – Banks partner with fintech companies and external service providers. QA must verify that these integrations do not introduce security vulnerabilities.
- Ensuring compliance across embedded services – Embedded banking services must comply with industry regulations, requiring QA teams to validate adherence to legal requirements.
- Performance testing for high transaction volumes – Digital banking platforms must handle large transaction loads without performance degradation.
A comprehensive QA suite ensures that digital banking products remain stable, secure, and compliant. Without proper testing, even a minor failure in an integrated financial service could disrupt an entire ecosystem, leading to financial and reputational damage.
Data-driven QA is shaping the future of quality assurance
Banks are moving toward data-driven QA to make smarter testing decisions. By leveraging real-time insights, organizations can optimize testing strategies, reduce redundancy, and focus on high-risk areas. Traditional testing often follows rigid test scripts, but modern QA teams use analytics and predictive modeling to identify critical failure points.
How data is improving QA in banking:
- Predictive analytics for risk assessment – AI-driven insights help prioritize test cases based on past defects and failure patterns.
- Real-time monitoring – Continuous monitoring of live banking applications provides instant alerts for potential issues.
- Adaptive testing strategies – Test automation can be adjusted dynamically based on real-world market conditions and user behaviors.
With banking applications evolving rapidly, relying solely on pre-defined test scenarios is no longer effective. Instead, banks must embrace QA approaches that continuously adapt to new risks and find potential weaknesses before they cause disruptions.
Protecting Sensitive Customer Data in the Software Development Process
In today’s banking landscape, protecting sensitive customer data is a non-negotiable responsibility. From the very beginning of the software development process, security must be embedded into every stage of the development lifecycle. It’s not enough to tack on a security check at the end—protection must be continuous and proactive.
This means rigorous testing of authentication flows, encrypted data storage, and secure account information handling. QA teams play a critical role in validating that applications never expose financial information or personal identifiers—especially during transactions, login processes, and mobile banking interactions. Regular penetration tests, vulnerability scanning, and dynamic application security testing (DAST) help uncover hidden weaknesses before they’re exploited.
Incorporating test automation makes it easier to consistently validate that data protection controls are functioning as intended. As more online banking platforms rely on third party services, protecting user data as it moves between systems becomes even more critical. QA teams must simulate real-world attack scenarios and test for edge cases that could compromise privacy.
Meeting the General Data Protection Regulation with Smarter QA
The General Data Protection Regulation (GDPR) has transformed the way the financial industry handles data, placing the customer in control of how their information is used. For banks and financial institutions, meeting GDPR requirements isn’t just about compliance—it’s about building trust.
QA testing under GDPR must go beyond functional validation. It must verify that personal data is collected only when necessary, with explicit consent, and that data subjects can easily request access, correction, or deletion of their records. Automated QA tools can test these workflows, flagging any gaps that may result in non-compliance or costly fines.
With a growing number of features relying on user acceptance testing, especially in banking apps, QA must ensure that opt-ins for marketing communications, data sharing, and cookie tracking are transparent and reversible. Additionally, test cases should include simulations where users withdraw consent to confirm that systems behave appropriately.
QA engineers also need to understand how data migration and backups affect compliance—ensuring personal data isn’t accidentally retained longer than legally allowed. Smarter QA practices enable banks to stay agile while maintaining full regulatory alignment.
Ensuring Quality in Credit and Debit Cards and Payment Gateways
Credit and debit cards are foundational to modern retail banking, and ensuring their secure use requires extensive QA. Whether it’s an in-store tap, a mobile wallet payment, or an online transaction through a payment gateway, banks must guarantee every interaction is seamless and secure.
End-to-end testing validates every stage of the card transaction lifecycle: cardholder authentication, authorization, funds validation, transaction posting, and reconciliation. For financial transactions, even a few milliseconds of delay or an error in calculation can result in financial losses or chargebacks. QA must simulate high volumes and peak loads to ensure the system handles real-world demands without crashing or exposing user data.
Test automation supports this by replicating transactions in bulk, performing negative testing, and verifying integration with systems such as fraud prevention engines, settlement systems, and alerts. QA teams also test account management flows to ensure that card issuance, blocking, renewal, and PIN management work without issues.
Similarly, payment gateways must undergo continuous security testing to validate that encryption, data tokenization, and redirection flows meet PCI DSS standards. A failure in any component could impact thousands of customers in real-time.
The Role of QA in Complying with the Payment Services Directive
The Payment Services Directive (PSD2) has been a catalyst for innovation in banking and financial services. It enables third-party services to access customer banking data—provided users give explicit consent—driving the growth of open banking and embedded finance. While this opens doors for new business models, it also introduces complex compliance and security challenges.
QA is instrumental in ensuring PSD2 compliance. For example, every time a mobile app uses an API to access account data or initiate a payment, QA must validate:
- Proper execution of multi-factor authentication
- Encryption and secure transmission of account information
- Logging and audit trails for all access attempts
Automating repetitive tasks like token expiration checks, consent flow validations, and timeout handling is crucial for speed and consistency. QA engineers must also test edge cases—what happens if a user revokes access or an API fails mid-transaction?
These scenarios are particularly important when financial institutions rely on external platforms or online platforms to deliver services. Ensuring these systems behave predictably under PSD2 mandates helps maintain customer satisfaction and regulatory compliance.
How to Achieve Desired Test Coverage in Complex Financial Systems
In the complex financial systems that underpin modern banking, achieving desired test coverage isn’t about testing everything—it’s about testing the right things. With hundreds of interconnected services, APIs, and user requests, QA teams must be strategic to deliver meaningful test results that support the business.
Traditional testing methods often follow rigid scripts, but modern banking QA requires agility. By leveraging tools powered by machine learning and NLP, teams can analyze historical bug data, customer complaints, and usage patterns to prioritize testing. This ensures the areas with the highest risk or customer visibility get the most scrutiny.
Financial systems need to be tested under multiple conditions—failures, high transaction volumes, simultaneous logins, and so on. Achieving meaningful coverage here also means testing integrations with existing systems, such as CRM tools, legacy databases, and digital solutions.
A mix of unit tests, integration tests, and full system validation ensures coverage at all levels of the development lifecycle. QA must be involved from early involvement in the development process to the final release and post-deployment monitoring.
When done right, this approach prevents defects from reaching production, improves system reliability, and helps ensure that users have a reliable experience, regardless of how they interact with the bank.
Wrapping up the trends in QA for banking: time to take informed decisions
As banks accelerate their digital transformation efforts, QA must evolve to address emerging risks, regulatory pressures, and customer expectations. A strong QA strategy ensures that banking software remains secure, compliant, and user-friendly while minimizing operational risks.
Investing in test automation, security testing, and AI-driven QA is no longer optional, but a necessity. Financial institutions that prioritize QA innovations will gain a competitive edge, reduce regulatory risks, and improve customer trust.
