Quality Engineering in Financial Services Today: TTC Global at QA Financial Forum Toronto 2026
From AI-augmented test automation to data integrity in banking, TTC Global shares what's on our minds ahead of the QA Financial Forum Toronto 2026
- Account Director, Canada
- TTC Global
- Toronto, ON, Canada
- 21 April 2026
As I prepare to attend the 2026 QA Financial Forum event in Toronto next week, I took some time to sit with the agenda and ask what it's really telling us about where the industry is.
With this year’s forum bringing together Heads of QA, testing, DevOps, and IT risk from banks and insurers across Canada and beyond, the conference themes (generative AI in testing, test data management, regulatory resilience, cloud migration) are anything but abstract. In fact, these themes parallel the conversations we've been having with financial services clients for the past several years. And they're conversations I think are about to get a lot more urgent.
The pressure financial services QA teams are under right now
If you work on a quality engineering team at a Canadian bank or insurer, you don't need me to tell you that the environment has changed. What used to be a relatively predictable release cycle has been compressed by competing forces.
On one side, there's the need to move faster. Financial services providers are constantly managing new payment models, real-time settlement expectations, digital-first customer interfaces, and the constant threat of a nimbler competitor launching what their customers want before they do. On the other side, there's the weight of an increasingly complex tech stack: legacy mainframes sitting alongside cloud infrastructure, integration layers multiplying as platforms connect to new services, and AI tools that teams are still struggling to scale enterprise wide. And, in the midst of all of this change, the regulatory space continues to tighten.
What I hear most from financial services QA leaders isn't "we need more testers." It's "we need a smarter approach to where we spend our testing effort, because we can't afford to test everything and we can't afford to miss what matters." Often, this question leads to a larger conversation about where they should focus their efforts:
AI Automation and Integrating AI Testing Tools
Data Integrity and Data Quality Engineering
De-risking Enterprise Software like SAP, Guidewire, and Duck Creek
These topics run through everything on the QA Financial Forum agenda this year. And it's something we've spent a lot of time thinking about at TTC Global. I’ll share some of our insights about these topics, where we have seen successes, and what QA teams should look out for when embarking on these transformations.
What AI actually looks like in practice and where the human still matters
Generative AI is the headline theme at this year's forum, and rightly so. But I think the most valuable conversations at events like this happen when we get past the headline and into the honest detail: what does AI-augmented testing actually look like in practice? Where does it accelerate things, and where does it still need a qualified human watching over it?
Our team has been doing applied R&D in this area through our Test Lab Research Series. In one experiment, our Auckland-based Principal Consultant Pavel Marunin led a project where, using an architect-guided AI agent swarm in Claude Code, we rebuilt a sophisticated Java automation framework in a single week. Claude estimated the same framework would have taken a senior automation architect up to seven weeks to build without AI assistance.
That's a 7x time saving on a real, production-bound framework.
But AI simply being “fast” isn’t enough to see these kinds of results. This experiment worked because an experienced architect was in the loop at every step, reviewing architectural plans before implementation proceeded, redirecting the AI when it created unnecessary abstractions, removing a Python script that somehow appeared in a Java repository, and identifying when AI-generated debugging was confidently wrong. Without that oversight, the framework would have been a complex, unmaintainable tangle.
The lesson we took from that experiment maps directly to financial services: the ROI from AI in testing is real, but it compounds dramatically when there's a qualified human guiding the architecture. In a regulated environment where the cost of a missed defect is a compliance failure, a customer impact, or a financial loss, that human oversight is what makes the AI-driven speed trustworthy.
The data problem nobody has fully solved yet
One challenge that keeps coming up across every financial services engagement we run is data. Specifically, how do you test at the scale and fidelity that financial systems demand, without either waiting months for the right production-like data or exposing sensitive customer information in your test environments?
Kayla Gillman (who will also be joining me at QA Financial) wrote about this in a blog discussing how to overcome data integrity testing challenges, particularly in the BFSI space. Kayla shares learnings from one of our clients, a major commercial bank processing millions of transactions. This client needed a framework that could compare datasets across heterogeneous sources — JDBC, CSV, APIs, LDAP — at scale, but getting data that actually reflected the edge cases that matter in production was a persistent challenge.
The approach we used combined Tricentis Data Integrity (which can test 400,000 rows of data per minute with a 95% automation rate) with Benerator by Rapidweller, a synthetic data generation tool that let us create representative test data without touching confidential customer records.
The outcome:
a 200% increase in automation coverage
50% of defects found in earlier testing phases
and a 35% reduction in regression testing time.
What I find most relevant about this for the Toronto audience is the regulatory dimension. Canadian financial institutions operate under some of the most rigorous data privacy and governance expectations in the world. That means the "test with production data" approach isn't just risky. In many cases, it's simply not possible.
The firms that are solving this well are the ones that have invested in a synthetic data strategy as a first-class part of their quality engineering practice, not as an afterthought.
A story from closer to home: A Canadian Insurer De-Risks SAP
Before I close, I want to share a recent SAP insurance story that's particularly relevant for an audience of Canadian insurance and financial services professionals.
Not long ago, TTC Global was brought into a Canadian insurer facing a high-stakes SAP upgrade. The program was running behind when we arrived. Regulatory obligations, financial reporting requirements, and a cautious client who had seen previous technology engagements underdeliver — it was the kind of environment where trust had to be earned, not assumed.
We made sure to enter the engagement with a goal in mind: bringing in focus. Using a risk-based quality engineering approach, we prioritized the SAP processes and integration points where failure would have had the highest business and regulatory impact, and we deprioritized proportionately where it made sense to do so. We introduced test automation with Tricentis Tosca with a view to building reusable assets the organization could carry forward into future SAP change.
The program was successfully delivered. And perhaps the most meaningful measure of success came in a routine update, when the client sponsor reflected that the engagement was going better than they could have imagined.
There's a reason I tell this story ahead of an event full of insurance and financial services QA leaders. The lessons from that engagement are exactly the principles that translate across every financial services transformation we support. They're also the principles that the QA Financial Forum has been surfacing year after year, in different ways, because they remain unsolved problems for most organizations.
Let's talk in Toronto
The QA Financial Forum Toronto takes place on April 23 at the Westin Harbour Castle. TTC Global is a proud sponsor this year.
Kayla and I will be at the TTC Global booth throughout the day. Feel free to stop by for a conversation (and yes, we'll have swag and a giveaway). Whether you want to dig into AI readiness for your QA function, think through a data integrity challenge, or just compare notes on what's working in financial services quality engineering right now, we'd love to hear from you.
If you'd like to arrange time in advance, feel free to connect with me on LinkedIn or reach out directly.
See you in Toronto!