QA Financial Forum New York | 15 May 2024 | BOOK TICKETS
Close this search box.

Artificial intelligence in quality assurance


Artificial intelligence (AI) is, in general terms, the set of technologies which appear to emulate human performance in narrow domains. Here follows a synopsis of articles by QA Media in 2019 on this topic.

In 2019, though AI enabled software risk management remained aspirational, it remains a topic of strong debate throughout financial services. Publications such as the World Wealth Report 2019 emphasise the need for financial firms to invest heavily in AI to retain wallet share.

UK regulators highlighted the increasing use of ML across the sector and announced plans to investigate what the future of regulation may entail in this era of AI.

“A key determinant of future competition will be whether data is used in the interests of consumers,” according to Christopher Woolard, Executive Director at the FCA. “We want boards to ask themselves: ‘what is the worst thing that can go wrong’ and mitigate against those risks.”

The Singapore government’s AI governance framework underscores the intent of regulators that AI systems should be developed according to ethical principles.

Nonetheless, the past 12 months have seen a flurry of product announcements and acquisitions, including: 

As the use of AI in quality assurance continues to emerge, it may ultimately enable firms to pursue a mission critical assurance strategy. Using predictive methods, firms can focus QA time and effort solely on business-critical applications, which may reduce risk for the systems that matter most.

“AI can help make smart decisions on a wider scale,” according to Yoram Mizrachi, CTO of Perfecto. “Using AI for self-healing in test execution enhances the ability of the system to learn.”

Whilst the operational use of AI in QA continues to emerge, the ability of the quality function to assure AI systems is also rising in importance. But testing AI systems is not magic: firms can leverage these simple steps to assure complex machine learning algorithms and reduce risk.

Ultimately, quality leaders at financial firms can future proof testing with AI by identifying the skills and business change required. Though firms are often faced with a buy versus build choice, foundational automation is emerging as the most effective path.