Following the success of our 2024 event, QA Financial held its 2025 edition of the QA Financial Forum Chicago.
The one day conference brought together leading experts in the financial services industry to discuss the latest challenges and opportunities in quality assurance and testing.
Key topics that were covered included automated testing, such as leveraging GenAI and agentic AI, test data management, including connecting data to test environments in the cloud, as well as emulation and virtualization technologies and platform-specific testing requirements.
Speakers also focused on embedding digital resilience and compliance in quality engineering, enterprise benchmarks for software quality and delegates discussed the business case for investment in new technologies.
Following a welcome address, Tavis Addison, Director of Business Process Optimization at Volkswagen Credit
Najat Mazroua, Senior Manager of Testing and Strategy at McDonald’s and Preeti Gupta, Executive Manager, Senior Director at BNY Mellon held a panel discussion on the future of software quality, as they zoomed in on key technologies, compliance and risk decisions for the era of AI.
What do firms need in order to adopt AI-driven quality assurance and robotic processes – processes that are so often disruptive to existing teams and platforms? How do they start? How much will they need to invest in new agentic coding and testing tools, and how will they benchmark their returns? And how do they assess vendor solutions as regulators increase their scrutiny of third party technology risks? The panelists shared their vision of how they want quality engineering to evolve over the course of 2025 and beyond.
Up next was Hod Rotem, Chief Product Evangelist at K2view who held a talk about cloning, emulating and transforming data for app testing with GenAI as banks, insurance companies and other large financial firms have to grapple with a key challenge in their transition to the Cloud. So how to create secure and regulatory compliant test data environments?
In this session panelists saw how synthetic and masked data can meet that challenge, and help accelerate software delivery. Rotem held a live demonstration of how AI can be used to generate production data, and how the customer data of different business entities can be cloned for performance testing of banking and insurance applications.
OpenText
Following a coffee break, Joe Hesse, Senior Account Executive at OpenText, delivered a talk on optimising DevSecOps for financial applications with AI as a key challenge for many firms is striking the right balance between rapid innovation and stringent security and compliance requirements.
How can your DevOps teams accelerate delivery without compromising quality? In this session Hesse discovered how AI-driven DevSecOps improves quality assurance, reduces vulnerabilities, and also streamlined workflows.
He explained how to implement DevSecOps principles and achieve agile and secure software delivery to meet the needs of your fast-changing financial marketplace.
Discover Financial Services
Following Hesse another well-known industry name took the stage, as Anuradha Veeravalli Murali, Senior Manager, Quality Engineering at Discover Financial Services spoke about testing, software risk and tooling decisions for the cloud.
Sharing her insights and experience from Discover’s Cloud-based transformation program, Murali covered how her team manages Discover’s test environments in the Cloud and the key vendor management and open-source tooling decisions they have made.
She paid special attention to how enterprise governance and IT risk standards are embedded in testing and QA, and a key takeaway for delegates from this session was a framework for connecting new DevOps technologies to your broader, future-proofed, DevOps strategy.
Ryan Speciality
With a career background in software quality assurance, Kerilyn O’Donnell, Vice President Business Applications at Ryan Speciality, now ensures a “quality first” mindset for application engineering at Ryan, the Chicago-based speciality insurer which handles $25bn+ of annual premium.
During her session, she explained how that mindset translates to everyday DevOps processes and how Ryan’s move to Cloud-based engineering is changing requirements for test data management and tooling. Kerilyn also discussed how changing business requirements are embedded in quality assurance standards.
Perforce
Following a tasty lunch, Clinton Sprauve, director of product marketing, application quality at Perforce, a provider of solutions to enterprise teams, spoke about leveraging AI for accuracy and clarity in testing, specifically testing complex financial charts and graphs.
Data-driven financial charts and graphics are vital to most financial firms conveying market insights too customers – but ensuring their integrity and real-time accuracy is challenging.
So how can we test the applications that are generating complex charts? “AI-driven validation with natural language prompts can enhance the testing of data-driven graphics, ensuring they reflect real-time, accurate financial information,” Sprauve explained.
“When you have a very complex chart or graph, it creates a bottleneck within that testing process.”
– Clinton Sprauve
“So what do most organisations currently do? Manual testing. Meaning they must sit there and view whether it’s supposed to look like that or they’re using screenshots, which is very unreliable,” he elaborated.
Instead, AI can easily take over, according to Sprauve. A bank that produces hundreds of charts and graphics on a daily basis, both for its customers as well as internal use, can now completely automatically check that the applications producing these charts are working correctly, using NLP.
How does this work? “NLP simplifies test creation by allowing users to write test scenarios in plain language,” Sprauve explained.
For instance, a user could input a prompt like “verify that the chart displaying US Equity Markets is correct” or is the Normalized Performance of SPX trending up or down in the graph”?
“The system can automatically generate the corresponding test. Also, it not only sees what is displayed on the screen but also understands the context and what is displayed,” he stressed.
A common fault it identifies could be if a chart displays incorrect data, such as showing the wrong values for the Nasdaq, S&P 500, Dow Jones indices, or a trending graph.
“In this case, AI Validation would recognise the request and cross-check the chart’s content with the expected values, flagging any discrepancies without human intervention,” he noted.
The tool Sprauve’s company Perforce uses for complex charts and graphics is Perforce Perfecto, a cloud-based testing platform that powers AI-driven test automation for mobile, web, and API testing. It is already widely used by customers, including a rage of banks, to accelerate testing, reduce manual intervention, and ensure high-quality digital experiences.
“AI is revolutionising how we test, because it understands the visual and contextual aspect of the actual image that it is viewing,” he elaborated. “So it analyses not only what it sees but also understands what is underneath.”
According to Sprauve, the example leveraging AI for testing charts is an example of how QA teams should prioritise a new approach in future.
“Typically how teams test today is that they are trying to build better scripts using AI co-pilots,” he said.
However, “that’s just creating more work and will shrink productivity. The goal should be for AI to eliminate the need for scripting. … The goal should be autonomous testing,” Sprauve concluded.
Data-driven financial charts and graphics are vital to most financial firms conveying market insights too customers – but ensuring their integrity and real-time accuracy is challenging.
So how can we test the applications that are generating complex charts? “AI-driven validation with natural language prompts can enhance the testing of data-driven graphics, ensuring they reflect real-time, accurate financial information,” Sprauve said.
GreatAmerica Financial Services
Sara Volden, Quality Assurance Leader at GreatAmerica Financial Services, also spoke at the event in Chicago.
Volden became the leader of the QA team at GreatAmerica in 2022. The QA team has been critical to the IT transformation program at this leading equipment leasing company. A major part of the transformation has been the migration of all leasing applications from a legacy management platform to Salesforce, and Volden focused on the testing requirements involved.
In addition to detailing the best practice in testing Salesforce, Volden discussed two broader technical challenges: connecting data management in the Cloud to testing requirements and how to realise the potential of low-code automated testing.
“Initially we thought we could just move the entire process over to SF however, after further consideration and discussion with the business we decided to move the process function by function, making it a gradual transformation vs a massive one-time transformation,” Volden shared.
“We had to consider how we would impact the business, how much training was needed, and the impact to our customers.”
So how did Volden manage the complexities of cloud-based data management, and what specific testing challenges emerged during this process?
“One advantage was that we were already familiar with Salesforce, having used it for other functions within our organization. From a testing perspective, we faced challenges such as data duplication for accounts and security issues related to user provisioning,” she explained.
“We needed to ensure that users could only access the information they were authorized to see. Additionally, we had to maintain data consistency across all our Salesforce environments.”
Volden said that her team’s biggest technical hurdle was that they over customized the legacy system making it difficult to transition some things into Salesforce. They solved this by breaking the process into smaller pieces to transition instead of doing a full transition at once.
Insights from RBC
An afternoon session organised by Anand Chawda, Director, Head of Quality Engineering, QTS – Strategic Programs at RBC Capital Markets, began with an introduction to the Shift Left strategies his team is employing at RBC Capital Markets and then moved on to focus on the key challenges of managing and migrating data from legacy systems.
He took us on a deep dive into the specific challenges of application testing and data transitions required for upgrades to Murex platform – the leading trading and risk management platform. Anand also covered business-driven testing requirements as well as operations and reporting requirements for Murex.
Next up the event turned its spotlight on GenAI with Amuthan Ganeshan, Principal Software Architect – Vice President at a US Bank, to share his vision for the next level of AI-driven testing for banking apps and APIs derives from his deep experience in the development of data science algorithms.
He demonstrated how you can leverage natural language coding to generate new data and test cases, and embed acceptance criteria in your DevOps process. Large financial firms face a common challenge in modernising complex legacy IT systems.
However, as Amuthan explained, if generative AI can be harnessed to deliver true test-driven development and transition to microservices and other new Cloud-related technologies, then it could be the key to revolutionary change.
Finally, the last speaker was Dan Belasich, Senior Software Engineer in Test at Enova International who plans to zoom in on a monorepo platform for code development, which requires significant upfront investment but – as Belasich explained – it’s a model that allows financial firms to more effectively manage development projects and enterprise-wide code quality.
A monorepo is a springboard for automation efforts and facilitates the introduction of AI technologies. Belasich demonstrated how user interfaces can be deployed and maintained in a monorepo and how his team Enovo is building its regression test suite with Playwright and then scaling up with GitHub Copilot.
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