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Yethi assists a leading bank in India in optimizing their payment processes

The Challenge

The bank had observed multiple functional and performance issues with their payment services across various channels. To ensure that their payment processes be safe and efficient, the bank needed a thorough evaluation of the same.

The Solutions

Yethi formulated a comprehensive test coverage plan, prepared test designs and executed the integrated test designs across the following payment channels in order to verify the key processes, end-to-end.

The Approach

Yethi developed test designs for payments initiated from Core Banking System, Internet Banking, Mobile Banking, UPI, ATM, POS and API (Application Programming Interface).

Then, Yethi executed the test cases through Test Condition matrix covering all possible scenarios, using Tenjin, Yethi’s robotic test engine.

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The Future of Software Testing with AI

Ever since technology has become a mainstay for modern businesses, software testing has emerged as a critical checkpoint for delivering reliable, secure, and user-friendly applications. As digital transformation accelerates, the complexity of software systems grows exponentially, making traditional testing methods insufficient to meet modern demands.

Enter Artificial Intelligence (AI)the game-changer that is reshaping the future of software testing. Today, AI-driven tools have become indispensable for businesses aiming to stay ahead. Let's explore how AI is revolutionizing testing and what the future holds.

Smarter Test Case Generation

Creating test cases has traditionally been a manual and time-consuming task. AI is transforming this by automatically analyzing application requirements, user behaviors, and system logs to generate optimal test cases.

Accelerated Regression Testing

Regression testing ensures that new code doesn't break existing functionality, but it's often repetitive and resource-intensive. AI-driven test automation reduces the time and effort required by identifying the most critical test cases and executing them efficiently.

Self-Healing Test Automation

One of the biggest challenges in test automation is maintaining scripts when the application changes. AI introduces self-healing capabilities, where test scripts automatically adapt to UI or API changes without human intervention.

Tools like Tenjin drastically reduce maintenance efforts and improves the resilience of automated tests with its self-healing mechanism.

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Optimizing Core Banking & Internet Banking Solutions

Greek Bank Leverages Yethi's Testing Services to Maximize Solution Performance Across Two of its European Subsidiaries

The Client

A leading bank in Greece offering a wide range of high-quality financial products and services in the domestic and international market.

The Challenge

With a growing customer base and implementation of new services via various disruptive digital banking channels, the client wanted to ensure that their Core Banking Solution (FLEXCUBE) and internet/mobile banking solution (OBDX) were robust and scalable enough to handle these augmentations.

The Solution

We carried out a comprehensive test to identify performance bottlenecks across Web, Application, Middleware, and Database Servers by verifying and validating the performance parameters of the following key applications/interfaces:

FCUBS Online Transactions

Payment Interface through MQ

ATM Interface

BO Queries + SMS Gateway Query

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How Self-Healing Test Automation Saves Time and Costs

In the fast-paced world of software development, time and cost are everything. Businesses strive to release software faster, with fewer bugs, and at a lower cost. But achieving this trifecta isn't easy, especially when test automation scripts break with even minor application updates. This is where self-healing test automation steps in a revolutionary approach that reduces maintenance headaches, accelerates testing cycles, and cuts costs.

In this blog, well explore what self-healing test automation is, why it's a game-changer, and how it saves your organization both time and money.

The Challenges of Traditional Test Automation

Test automation was created to speed up testing processes, but it often comes with its own set of challenges:

Fragile Test Scripts: Test scripts in traditional automation are highly sensitive to application changes. If the UI or API undergoes even minor updates, the scripts fail, requiring manual intervention.

High Maintenance Costs: QA teams spend a significant amount of time fixing broken test scripts, diverting resources from actual testing.

Delayed Releases: Fixing and re-running tests can slow down the development cycle, delaying time-to-market.

Limited Scalability: As applications grow in complexity, maintaining thousands of test cases becomes a daunting task.

These challenges make it clear that traditional test automation isn't always the silver bullet it promises to be. Enter self-healing test automation, a solution designed to address these very pain points.

What Is Self-Healing Test Automation?

Self-healing test automation is an advanced capability that leverages AI and machine learning to identify and fix broken test scripts automatically.

Here's how it works:

Dynamic Element Identification: When a UI or API element changes (e.g., a button ID or field name), the system dynamically identifies the updated element using contextual clues like attributes, labels, and hierarchy.

Automatic Script Updates: Instead of flagging a failure, the system updates the test script in real-time to align with the new application structure.

AI-Powered Insights: The system learns from previous changes to improve its accuracy in predicting and resolving future issues.

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The Role of ISO 20022 in Driving Efficiency and Interoperability in Modern Payments

ISO, the International Organization for Standardization, is transforming the global payments landscape by standardizing messaging formats to ISO 20022 for faster, more structured, and interoperable data exchange between financial institutions. This shift enhances data quality, automation, and cross-border payment efficiency, driving the future of financial messaging. The ISO 20022 messaging is more than just meeting compliance standards, it represents a strategic transformation that empowers financial institutions to tap into the benefits of seamless interoperability and compliance. This change sparks innovation and enhances service offerings across the board.

By promoting consistency, enabling richer data exchange, and improving interoperability, ISO 20022 is shaping a smarter future for financial data.

The Purpose of ISO 20022

The International Organization for Standardization (ISO) developed ISO 20022 to overcome the limitations of present financial messaging standards. Traditional messaging formats often face challenges due to unstable data structures, restricted data-carrying capacity, and lack of global interoperability. ISO 20022 resolves these challenges by offering a detailed and adaptable framework for financial messaging.

The new standard includes widely used methodology, the company model, and the storage of message definition. With these components, organizations can establish standardized communication processes, reduce technical difficulty, and open new opportunities for modern technology.

Key Benefits of ISO 20022

ISO 20022 messages are essential for instant payments and contribute significantly to the extended modernization of payment processes. This ability is essential for advancements such as transferring from the end-of-day batch file updating to the real-time payment process. Also, ISO 20022 improves analytics, which can result in the provision of valuable new levels of payment services to the customers of financial institutions.

Financial institutions can improve their operational efficiency using the new ISO format, which allows the exchange of detailed payment information in addition to customer interactions. This promotes seamless processing, minimizes errors, and reduces the necessity for manual intervention. Learn More

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Steps Banks are taking towards Digital Transformation Journey

The digital transformation journey is gaining momentum in the banking and financial industry. But when did the journey first start? As per a Gartner report, 69% of organizations believe that the Covid-19 global pandemic has accelerated the digital initiatives, 60% have increased the focus on improving the operational excellence through digital businesses, 50% have increased the focus to drive higher levels of cost optimization through digital initiatives, and 48% are investing on digital initiatives. Covid-19 may not be the sole reason for the growing digital business, but it has sparked the growth.

Banks are adopting new technologies on their digital transformation journey. They are taking every step to digitize the services and move processes online. Banks are also accommodating backend changes which is an essential step towards digitalization and supports the transformation. Any digital move is incomplete without ensuring security against scams and fraud risks. Banks initiate many digital moves but eventually strive to succeed or manage the pace. It is because they lack support or skill in many aspects, which are essential to compete with digital-native solutions. Digital transformation does not end with the integration of digital systems. It is a move that includes meticulous attention to ensuring user experience, security and performance of applications, monitoring infrastructure and more.

In this article, we will explore the objectives and goals banks and financial institutions are setting up for themselves and the steps they are taking toward the business transformation journey.

Goals and objectives for digital transformation journey in banks and financial institutions

The digital transformation journey of banks and financial institutions did not start overnight. There was a pre-conceived idea behind such a revolution. Banks and financial institutions envisioned their goals and objectives before their journey. Let’s explore them one by one.

Improving customer experiences

Customer requirements have changed with the passing years. Today, customer demands quick and easy solutions. They require digital solutions (mobile devices & digital apps) integrated with new technologies, improved use of data and analytics and more. It enables the customers to get the exact services quickly whenever they want them.

Consumer behavior has changed the way banks look and offer services to their customers. Banks and financial institutions are customizing digital innovation to create digital engagement for customers of different segments, based on the factors such as age, preference, location and more. Consumers have moved to digital platforms from the traditional model of banking automation, which facilitates digital transformation.

Consumer’s changing needs are compelling banks to re-evaluate the services and future scope of the branch network. Banks are increasing their digital footprint by moving the branch footprint to a digital platform. They are aligning the services to meet consumer behavior and building new omnichannel sales and service models. Based on consumer trends, they are enhancing cross-channel marketing communications.

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Major Challeneges in API testing

API testing plays a critical role in validating the functionality, reliability, performance, and security of applications. However, it comes with a unique set of challenges that testers must navigate to ensure robust integration between services.

One of the primary challenges is the lack of proper documentation, which makes understanding request/response formats and expected behavior difficult. Additionally, data dependency and environment instability often result in inconsistent test results, especially in shared testing or staging environments.

Another major issue is handling complex authentication mechanisms like OAuth2, JWT, or token refresh cycles, which require additional logic and security considerations during test automation. Test data creation and maintenance also pose problems, particularly when APIs require dynamic inputs or data chaining across multiple endpoints.

Validating response structures, including nested JSON or XML, and ensuring backward compatibility during versioning are also critical pain points. Moreover, integrating API testing into CI/CD pipelines demands a scalable and stable framework.

This blog will explore each of these challenges in detail, along with strategies and tools (like Postman, Rest Assured, and Swagger) to overcome them—enabling QA teams to ensure seamless API performance in complex, distributed systems.

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AI in Performance Testing: Ultimate Guide 2025

In 2025, performance testing is no longer just about simulating user load—it’s about leveraging Artificial Intelligence (AI) to predict, detect, and resolve performance issues proactively. AI has become a game-changer, transforming how organizations approach scalability, speed, and reliability testing.

AI-driven performance testing tools can analyze massive datasets in real time, identifying trends and anomalies that traditional tools may overlook. Machine learning models help forecast potential bottlenecks under varying user loads, enabling teams to take preemptive action before issues impact users. AI also improves test coverage by simulating realistic user behavior and usage patterns, ensuring more accurate performance insights.

One of the most powerful applications of AI is in auto-tuning performance tests, where it dynamically adjusts parameters based on real-time feedback. It also supports continuous performance testing in CI/CD pipelines, reducing manual intervention and speeding up release cycles.

Visual dashboards powered by AI test automation simplify complex metrics, making it easier for stakeholders to make informed decisions. As cloud infrastructure becomes more complex, AI ensures faster root-cause analysis and intelligent alerting.

Whether you're testing microservices, APIs, or enterprise applications, AI helps you move from reactive to predictive performance engineering. 2025 is the year to embrace AI-powered performance testing for smarter, faster, and more resilient systems.

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AI-Driven Test Automation: The Future of Intelligent Software Testing

The future of software testing is intelligent, adaptive, and AI-driven. As businesses demand faster releases and higher software quality, traditional test automation often falls short—requiring constant maintenance and manual oversight. Enter AI-driven test automation, a game-changer that is reshaping how QA teams operate.

AI algorithms can now generate test cases, detect UI changes, self-heal broken test scripts, and analyze large volumes of test data to identify patterns and anomalies. This shift from rule-based to data-driven automation reduces human intervention and accelerates testing cycles—making it a perfect fit for agile and DevOps environments.

In the United States, where tech innovation is moving at a rapid pace, AI Powered Testing Tools USA is being adopted across industries such as finance, healthcare, and SaaS. These intelligent systems not only ensure faster time-to-market but also enhance test coverage and accuracy by continuously learning from application behavior.

Moreover, AI empowers QA teams to focus on strategy and user experience, rather than routine test maintenance. With predictive analytics, risk-based testing, and NLP-based test creation, AI-driven automation is not just an upgrade—it’s the future.

For forward-thinking organizations, adopting AI in test automation is no longer optional—it’s essential for staying competitive.

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How AI Can Augment Manual Testing

While automation has taken center stage in QA, manual testing remains essential—especially for exploratory, usability, and ad hoc testing. In 2025, AI is playing a crucial role in augmenting manual testing, making it faster, smarter, and more insightful without fully replacing the human touch.

AI can assist manual testers by identifying high-risk areas in an application based on historical defect data, allowing testers to focus their efforts where it matters most. Intelligent test prioritization and recommendation systems guide testers on which scenarios to explore, reducing redundant efforts and improving test coverage.

Natural Language Processing (NLP) allows AI testing tools to convert manual test cases written in plain English into automated suggestions or scripts, helping testers bridge the gap between manual and automated testing. AI-powered visual recognition can also aid in UI testing by detecting visual bugs that might be missed by the human eye.

Additionally, AI tools can analyze test execution data, identify patterns, and highlight anomalies or flaky behaviors, supporting decision-making during test cycles. This enables manual testers to spend less time on repetitive checks and more on creative, exploratory testing.

In essence, AI empowers manual testers with data-driven insights and intelligent assistance, enhancing the overall quality assurance.

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What is Shift-Left Testing – A Comprehensive Guide

Shift-left testing is a software testing approach that emphasizes performing tests earlier in the development lifecycle. Traditionally, testing was a phase that followed development. In contrast, shift-left testing integrates testing activities from the very beginning—right from requirements gathering to coding.

The core idea is simple: identify and fix bugs early, when they are easier, faster, and less expensive to resolve. In agile and DevOps-driven environments, this is critical for delivering quality software at speed.

Shift-left testing involves practices like test-driven development (TDD), behavior-driven development (BDD), and continuous integration (CI). Developers, testers, and product teams collaborate closely to define test cases before code is even written, ensuring alignment with business goals.

Benefits of shift-left testing include reduced defect leakage, faster feedback loops, improved code quality, and shorter release cycles. It also fosters a quality-first mindset across the team.

Tools commonly used in shift left testing include JUnit, TestNG, Selenium, Postman, and CI tools like Jenkins and GitHub Actions.

In summary, shift-left testing is not just a technique—it’s a mindset that enables proactive quality assurance. As software delivery becomes faster and more complex, shift-left testing is key to maintaining high standards without slowing down innovation.

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How Payment Modernization is Transforming Financial Transactions in the US?

The US payments landscape is undergoing a rapid transformation, driven by digital innovation, regulatory shifts, and evolving consumer expectations. At the heart of this change is payment modernization — the overhaul of legacy systems to enable faster, safer, and more seamless transactions across banking and financial services.

Initiatives like FedNow, the Federal Reserve’s real-time payments system, and the growth of open banking APIs are accelerating instant payment systems and enabling financial institutions to offer 24/7 transaction capabilities. Consumers and businesses now expect frictionless digital experiences, prompting banks to adopt cloud-based infrastructure, AI-powered fraud detection, and blockchain-led settlements.

For corporates, modernization means streamlined B2B payments, improved cash flow visibility, and enhanced cross-border capabilities. For consumers, it enables contactless payments, P2P transfers, and mobile-first experiences.

Moreover, payment modernization supports regulatory compliance and financial inclusion, allowing underserved communities better access to financial tools. As the US aligns with global trends, collaboration between fintechs, traditional banks, and regulators becomes crucial in shaping the future of payments.

The result is a dynamic, resilient payment ecosystem that is faster, more transparent, and tailored to meet the demands of today’s digital economy.

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The Ultimate Guide to Building a QA Automation Strategy That Delivers Results

Building a QA automation strategy that truly delivers results goes beyond just picking tools—it's about aligning testing efforts with business goals, enhancing quality, and accelerating releases. This ultimate guide walks you through the essential steps to build a strategy that works.

Define Measurable Goals

Start by outlining what “results” mean for your team—faster releases, fewer bugs in production, improved test coverage, or all of the above. Clear KPIs drive focused automation efforts.

Assess Current QA Maturity

Evaluate your team’s existing testing processes, tools, and skills. Understanding your baseline helps identify gaps and opportunities.

Select the Right Tools & Tech Stack

Choose Test Management tools that align with your application type and team expertise. Consider scalability, integration with CI/CD, and community support.

Identify High-ROI Test Cases

Not everything should be automated. Focus on stable, frequently used, and business-critical scenarios first.

Build a Sustainable Automation Framework

Create a modular, maintainable test architecture with version control, reusable functions, and clear documentation.

Monitor, Measure, and Iterate

Use metrics to evaluate effectiveness—test execution time, pass/fail rates, defect trends—and optimize regularly.

With the right mindset and structure, your QA automation strategy can become a key driver of product quality and delivery speed.

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How can banks change Innovate in Transactional Banking to stay Competitive

In today’s fast-evolving financial landscape, banks must innovate in transactional banking to stay competitive and meet rising customer expectations. Traditional models are no longer sufficient—clients demand faster, seamless, and secure digital services. To stay ahead, banks should invest in real-time payments, AI-driven automation, and open banking platforms that enable collaboration with fintechs.

Leveraging APIs can transform transactional banking by enhancing integration and customer experience across platforms. For instance, offering smart dashboards, cash flow forecasting tools, and automated reconciliation services can provide value-added services to corporate clients. Artificial Intelligence (AI) and Machine Learning (ML) can be utilized to detect fraud in real time and personalize offerings based on transaction behaviors.

Embracing blockchain for faster, tamper-proof cross-border payments and digital identity verification is another avenue for innovation. Additionally, sustainability-focused solutions such as green payment options or ESG-linked transactional banking services are gaining traction.

The future of transactional banking lies in agility and co-creation. Banks that transform from product providers to service partners by building digital ecosystems and fostering innovation will not only retain market share but also create new revenue streams. Investing in customer-centric, tech-forward strategies is the key to long-term competitiveness in transactional banking.

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Change Management in Unpredictable Times

In today’s volatile business landscape, change is no longer a phase — it’s a constant. Economic shifts, technological disruption, geopolitical uncertainty, and evolving customer expectations are forcing organizations to adapt faster than ever. In such unpredictable times, effective change management becomes not just important, but mission-critical.

Companies that navigate change successfully focus on people, communication, and adaptability. Change management isn't just about processes — it's about preparing employees, aligning leadership, and maintaining clarity even amid chaos. Without a structured approach, even the best strategies can fail due to resistance, confusion, or misalignment.

Consultants play a key role in managing this complexity. They offer objective insights, proven frameworks, and the agility needed to respond in real time. Whether it's shifting to hybrid work, undergoing digital transformation, or restructuring teams, a consultant-led approach ensures that transitions are strategic, smooth, and sustainable.

Crucially, successful change management consulting services creates a culture of resilience. It empowers teams to not just survive disruption, but thrive through it — turning uncertainty into opportunity.

Now more than ever, businesses must embrace change as a continuous journey. With the right guidance, even the most unpredictable times can lead to powerful transformation.

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Avoiding Common Pitfalls of Automation Testing

Automation testing brings speed, consistency, and scalability to software quality assurance—but it’s not without its pitfalls. One of the most common mistakes is automating unstable or frequently changing features, leading to high maintenance and flaky test cases. Start by identifying stable, high-value test scenarios before automating.

Another trap is ignoring test design principles. Poorly structured test scripts become difficult to manage and debug. Implement best practices like the Page Object Model (POM) and maintain a clear folder structure to improve readability and scalability. Over-reliance on record-and-playback tools without customization is another issue, as it leads to fragile test cases.

Lack of proper assertions, hard-coded data, and not using version control can cause inconsistencies across environments. Always integrate data-driven testing and use a version-controlled test repository.

Teams also neglect CI/CD integration, missing out on real-time feedback and continuous quality checks. Lastly, measure ROI regularly—AI test automation is an investment, and without monitoring outcomes, teams may spend more effort maintaining tests than gaining value.

By addressing these pitfalls early, teams can build a robust automation strategy that saves time, enhances coverage, and drives faster, quality releases.

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How to Reduce Testing Cycle Time?

In an agile and fast-paced development environment, reducing testing cycle time is critical for faster releases and maintaining high-quality standards. Long testing cycles can delay go-to-market timelines and increase costs. Fortunately, with the right strategies, QA teams can speed up the process without compromising accuracy.

Start by shifting left—introduce testing early in the development lifecycle. Catching defects earlier reduces rework and keeps the release on track. Next, focus on AI automation testing for repetitive and regression testing tasks. Use tools like Selenium, TestNG, or AI-powered platforms such as Testim and Tenjin AI to execute faster, smarter tests.

Parallel testing is another game-changer. Run multiple tests across different environments simultaneously to reduce execution time significantly. Additionally, implement risk-based testing to prioritize high-impact test cases and optimize coverage.

Integrating testing into your CI/CD pipeline ensures continuous feedback and enables quicker resolution of issues. Also, adopt modular test case design—smaller, reusable test scripts are easier to manage and update.

Finally, promote collaboration between developers and testers using tools like Jira, GitHub, or TestRail to streamline workflows.

By combining automation, intelligent test prioritization, and better collaboration, you can significantly reduce testing cycle time and deliver high-quality software, faster.