The rush to deploy artificial intelligence across financial services is creating a growing quality and resilience challenge for QA and software testing teams, as many AI systems entering production environments fail to meet basic software engineering standards.
A new report analysing AI systems in private equity portfolios suggests that while investment and experimentation with AI are accelerating rapidly, engineering maturity, testing discipline and software quality are lagging behind.
For banks, insurers, private equity firms and fintechs integrating AI into core platforms and decision systems, the findings raise serious questions about reliability, governance and operational risk.
The report, AI Ambition and Reality in Private Equity, published by Software Improvement Group, highlights a widening gap between organisations’ AI ambitions and the robustness of the systems being deployed.
According to the study, many organisations are struggling to move AI initiatives beyond experimental pilots into stable, enterprise-grade systems.
For financial institutions operating in heavily regulated environments, the implications are clear. Fragile models running in production environments, poor maintainability of AI systems and difficulty scaling pilots into enterprise platforms can quickly translate into operational risk, regulatory exposure and customer-impacting failures.
“Attackers no longer need to breach financial systems; they can influence it.”
– Software Improvement Group
One of the most striking findings relates directly to software quality. “72% of AI systems score below our recommended build-quality threshold, which can lead to significant risks and quality issues,” the report stated.
For QA and quality engineering teams inside banks and insurers, this highlights a structural issue: AI systems are often being deployed without the same level of engineering discipline applied to traditional software platforms.
The report stresses that moving from experimental prototypes to production-grade AI requires far more rigorous engineering practices.
“Many organisations struggle to translate AI pilots and experiments into scalable, secure, compliant, and maintainable real-world applications.”
New testing challenges
Beyond model quality, the report also highlights the growing role of generative AI in writing software code itself, a development that introduces additional testing and security challenges for enterprise technology teams.
While AI-assisted coding tools are becoming widely used across software engineering teams, the study suggests that the reliability of machine-generated code remains inconsistent.
“Only a small fraction of these systems works correctly without modification, limiting their practical use and overall reliability,” the aurhors wrote.
For QA teams responsible for validating enterprise applications, this creates new demands around code validation, regression testing and security testing.
The report also identifies increased vulnerability risks in AI-generated code. “AI struggles more with writing secure code than humans. On average, AI showed double the amount of security risk violations compared to human projects.”
In financial services environments where security, compliance and auditability are essential, this raises concerns about the quality controls applied to AI-assisted development workflows.
New forms of testing and governance
Another key challenge singled out in the report is that AI systems behave fundamentally differently from traditional deterministic software, requiring new testing strategies and governance frameworks.
Unlike conventional applications, AI systems learn from data, evolve over time and may produce probabilistic outputs rather than fixed responses.
“AI systems are software systems and should be treated as such,” the report stated, while noting that they “learn, adapt, and operate with a level of autonomy that traditional systems don’t.”
For financial institutions, this means quality engineering teams must expand testing beyond traditional application code to include training data, model behaviour, pipelines and decision outputs.
“Banks struggle to translate AI pilots and experiments into scalable, secure, compliant, and maintainable real-world apps.”
– SIG report
The report also highlights emerging security risks unique to AI systems. Attackers, it notes, can manipulate or influence AI behaviour without directly breaching infrastructure. “Attackers no longer need to breach financial systems; they can influence it.”
This shift is driving the need for new testing approaches such as adversarial testing, data integrity validation and AI model robustness testing.
AI adoption remains experimental
Despite the rapid growth of AI investment across industries, the report also suggests that production-grade AI deployments remain relatively limited.
The analysis found that only a small proportion of enterprise systems currently qualify as true AI systems.
“From all the systems in production analysed, roughly 1.5% qualify as an AI system.”
For banks, insurers and private equity-backed fintech companies exploring AI-driven innovation, this suggests that much of the current activity remains at the pilot or experimental stage.
Yet as financial institutions begin integrating AI into high-risk use cases such as fraud detection, credit decisioning and operational automation, the pressure on QA and testing teams will intensify.
Without robust testing frameworks, governance models and engineering discipline, fragile AI systems can quickly introduce instability into complex financial technology environments.
The report therefore reinforces a growing message for financial services technology leaders: successful AI adoption depends not only on advanced models and algorithms, but also on the quality engineering, testing frameworks and governance processes needed to ensure those systems remain reliable, secure and resilient at scale.
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Looking for more news on regulations and compliance requirements driving developments in software quality engineering at financial firms? Visit our dedicated Regulation & Compliance page here.
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