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5th March 2025
Ask any financial services leader what slows down transformation, and chances are they’ll say the same thing: testing. It’s a critical part of any technology transformation project, and when it’s not handled efficiently, it can lead to missed deadlines, compliance challenges, and costly production issues.
Balancing speed and accuracy in testing is key - and that’s where AI-powered testing is making a difference. By enhancing traditional testing approaches with automation and intelligence, teams can accelerate delivery without increasing risk.
This blog draws on insights from a recent roundtable event hosted by 2i and the follow-up report, Growing Financial Services with AI. Senior technology leaders from across the industry made one thing clear: AI in financial services only works if it’s built on a rock-solid foundation of quality and control. Otherwise, organisations risk compliance failures, reputational damage, and very awkward conversations with the board.
To keep transformation on track, on budget, and out of the headlines, financial institutions need to focus on three key areas:
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Automate testing for speed and accuracy
Manual testing still plays an essential role in complex, high-risk scenarios where human expertise is invaluable. However, when testing relies too heavily on manual processes, delivery teams can struggle to keep pace with transformation demands. This is where AI-powered automation can deliver significant value.
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Accelerate test execution - automating repetitive test cases to free up teams for high-value analysis.
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Enhance test coverage – generating thousands of test scenarios to identify risks faster.
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Improve accuracy and efficiency – reducing human error while ensuring compliance requirements are met.
“One of the things our team produced early in our own PoC process was the ability to generate test cases automatically. We proved a technique for taking a set of requirements and creating use cases for testing.”
-Dave Kelly, CEO, 2i
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Use continuous validation to prevent compliance failures
AI models change over time, which is great, until they start drifting out of compliance and making bad decisions. Without continuous validation, an AI model that worked perfectly last month might suddenly be approving dodgy transactions or mispricing risk. Not ideal.
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Keep AI models accurate and bias-free - AI models learn as they go but that also means they can drift into murky waters. Continuous validation ensures models stay accurate, fair, and regulator approved.
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Automate regulatory compliance checks - Regulators love paperwork. AI-driven testing ensures every system update meets compliance requirements before deployment, so you’re always one step ahead of the auditors.
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Fix issues before they make headlines - No one wants to find out about a security flaw via a front-page news story. AI-powered testing catches and resolves compliance issues before they become business risks.
“The leadership appears to be more reactive, addressing issues only when they occur, rather than proactively identifying areas where quality gates should be implemented.”
-Tijo T Joy, CTO, Azur Technology
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Leverage synthetic data for large-scale, secure testing
Testing AI models in financial services is tricky - you need realistic data, but you can’t risk using actual customer information. Enter synthetic data: AI-generated datasets that mimic real-world financial transactions, without the privacy nightmares.
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Create realistic test environments with zero compliance risk - Synthetic data replicates real customer behaviour, allowing AI models to be tested safely without using actual personal data.
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Reduce security vulnerabilities and regulatory headaches - Unlike traditional test environments, synthetic data completely removes the risk of exposing PII, making it a compliance team’s dream.
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Scale testing without waiting for real data - No more waiting around for datasets to be approved. With synthetic data, firms can test AI-powered applications at scale, without regulatory roadblocks slowing them down.
“Maybe in ten years, there will be a CQO, a Chief Quality Officer. The responsibilities of the CQO will extend beyond managing risks, governance, and compliance of delivered products and services. They will also include measuring quality of innovations.”
-Isabel Palmer, Director of Engineering & Transformation, Experian UK
Technology transformation shouldn’t feel like a high-stakes gamble. AI-powered testing ensures that every new product launch, system update, or regulatory shift happens on time, on budget, and with zero nasty surprises.
Want to find out how industry leaders are navigating the introduction of AI into their technology transformation? Download the full 2i roundtable report for exclusive insights.
Or, are you looking for a smarter way to test and launch AI in financial services? Get in touch to discover how 2i helps financial institutions deliver transformation without the headaches.