As banks grapple with increasingly complex partner ecosystems and regulatory scrutiny, automated test data management is emerging as a critical pillar of enterprise quality engineering.
Speaking ahead of the QA Financial Forum New York 2026, on 12 May, Bread Financial’s Rakesh Sukla discusses how data, automation and AI are reshaping testing strategies in highly regulated environments.
The challenge of managing test data at scale is becoming one of the defining issues for quality engineering teams in banking and financial services, particularly as institutions operate across increasingly complex partner networks and product configurations.
“Companies in this space in highly regulated companies like Bread Financial and other fintech in this space, we do face the issues around test data,” said Rakesh Sukla, Director of Engineering and Platform Automation Engineering at Bread Financial, speaking on the QA Financial Podcast ahead of his appearance at the QA Financial Forum in New York City.
“I would definitely categorise this as one of the very top things faced by pretty much across the industry. It’s definitely a huge problem to solve.”
At Bread Financial, where hundreds of partner configurations underpin credit card and payments products, the complexity extends far beyond simple data provisioning.
“We support like thousands of partners with very different configuration,” Sukla explained. “The challenging part here is not just limited to configuration. It’s like when we work with partners, they are looking for data in a very full state.”
That “full state” requirement reflects the need to simulate production-like journeys across multiple systems.
“It means that the transaction has to go through our underwriting system, our fraud system. It has to go through our servicing, loan servicing, then you have rewards. Then you have statements,” he said. “Really creating a data which looks like a production data very quickly is what makes things really challenging.”
The situation is further complicated by the nature of financial systems themselves.
“Things like underwriting and fraud systems are not very easy to tweak with,” Sukla noted. “There are many parts of this journey which are not real time. It happens overnight or happens over the batch.”
As a result, traditional approaches to test data management are increasingly untenable in large-scale financial environments.
“The traditional practice, I would say, is not really scalable because it’s manual, it is written in spreadsheets, people are going through line by line in that,” he said. “Those things are not scalable.”
He added that such approaches can become a direct bottleneck to delivery: “The approach is also a huge bottleneck in really delivering value to the client very quickly.”
Embedding data automation into the SDLC
To address these challenges, Bread Financial has focused on building automated, on-demand data capabilities embedded directly into the software development lifecycle.
“We try to automate and provide data on demand as much as possible,” Sukla said. “Even if something is super complicated and can take few days to create, we do provide you a workflow in which you trigger it and the data will be available when it is available.”
This includes integrated notification systems to support asynchronous workflows. “We will email you. We will notify you on Slack and Teams,” he added.
According to Sukla, the organisation has already achieved significant coverage.
“At least the general perspective and we have done like I would say 90 % of it. We do have capability to provide you data automated and on demand through our self service portal.”
“Things like underwriting and fraud systems are not very easy to tweak with. They are not real time.”
– Rakesh Sukla
Crucially, these capabilities are not limited to user interfaces but extend into engineering workflows.
“The way you create stuff through the portal, we also provide you restful APIs that you can integrate in your automated scripts or you can integrate in your CI CD journey,” he said. “So lot of different ways in which you can trigger our automated process to get data wherever you need it.”
This integration enables test data provisioning to become a native part of continuous delivery pipelines, rather than a separate, manual process.
Test data, incidents and compliance converge
As regulatory expectations around resilience and governance intensify, the role of test data is also expanding beyond testing into broader risk and incident management frameworks.
“From an engineering point of view is every test data is an asset,” Sukla said. “And who created the data, where exactly data is being used, we have complete metadata for it.”
This traceability becomes critical during incident response.
“Generally when an incident happens, like the first thing that we want to do is exactly reproduce what happened in production in our lower environment,” he explained. “So in that particular point of view, like the quality of data and how quickly you can create it is really paramount.”
The ability to rapidly generate realistic datasets underpins both root cause analysis and release assurance.
“All these things ultimately tied together, like when incident happens, like we want to quickly reproduce why it has happened from where the issue comes from,” Sukla said. “And ultimately these impacts the release process and also like kind of like it’s also a part of our quality engineering strategy as well.”
Observability becomes a ‘first-class citizen’
Alongside data automation, Bread Financial has prioritised observability and monitoring as core components of its testing framework.
“Testing is taken very, very seriously at Bread Financial. In the same way, storing logs and monitoring and observability are also a first-class citizen,” Sukla said.
The firm has embedded integrations with monitoring platforms to provide end-to-end visibility across distributed systems.
“Anytime an issue happens in our framework, let’s say at a test client level, our integration provides you that complete visibility into the distributed error log,” he explained.
This enables teams to trace issues across multiple layers of the technology stack.
“You can really see the error happened at a client level, but where exactly error originated from? Is it at a service level or is it a database level?”
He added: “For every single error we provide distributed error tracing. You can figure it out from there from what really happened going through all the logs.”
AI set to reshape testing strategies
Looking ahead, Sukla expects artificial intelligence to fundamentally reshape both software engineering and quality engineering practices in the near term.
“We have been experimenting with AI features quite a bit at Bread Financial,” he said. “I think looking towards the end of this year, how we do stuff in quality engineering and also in software engineering in general will look very, very different.”
He pointed to a shift in how teams approach problem-solving and test strategy design.
“When you have a problem we used to first use like, how do we convert this particular business requirement into a great strategy and we brainstorm and jump on a meeting and try to figure it out,” Sukla said. “Gone are those days, now AI can do once you throw good context around it, AI can do quite a bit of that.”
“Towards the end of this year, how we do stuff in QE and in software engineering will look very, very different.”
– Rakesh Sukla
AI is also playing a growing role in risk-based testing and orchestration.
“You’re talking about risk-based prioritization and execution. We’re launching a platform in two weeks at Bread, which will allow you to define risk profile for your applications,” he revealed.
“And based on the risk profile, we figured it out what will be the impacted area and how many tests we need to run because it’s a very high risk module in your application.”
While human oversight remains important, Sukla sees AI as a major accelerator.
“I definitely feel like AI will be a huge force multiplier,” he said. “There will be bits and pieces, which will be still humanly driven, but AI will definitely take a very center stage in orchestration in execution and prioritization.”
From data to decisions: the next frontier
The forthcoming platform reflects a broader shift towards data-driven test optimisation.
“Given a code change in a big distributed system, how do you correlate the code change to the impacted test suites and only run that particular test suite and provide you the quick feedback?” Sukla said. “That has been a pretty complex problem.”
To address this, the platform aggregates multiple data inputs across the development lifecycle.
“When you make a code change, we collect data. When you make a test script change, we collect the data and there are all sorts of other stuff coming into our platform,” he explained.
This enables more targeted and efficient test execution.
“You have made this change in this particular part of the code, how this will tie up and ultimately filter out those tests and run it for you.”
As financial institutions continue to scale digital platforms under increasing regulatory pressure, such approaches are likely to become central to enterprise QA strategies.
Sukla will explore these themes in more detail at the QA Financial Forum New York 2026 on 12 May, where industry leaders will gather to discuss the evolving role of testing, automation and AI in strengthening resilience across financial services.
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