As digital transformation accelerates across the financial services sector, quality assurance teams face a reckoning. The rapid rise of generative AI, the explosion in data volumes, and the limits of conventional computing are pushing software testing practices, and infrastructure, toward a fundamental shift.
In its latest Next Gen Tech: Computing report, Bank of America Institute highlights the five breakthrough computing trends that will transform how banks build, test, and secure the digital systems of the future, with, by far, quantum computing getting the most attention.
“We live in a world of exponential data growth,” wrote Vanessa Cook, strategist at Bank of America Institute. “The number of connected devices is expected to reach 350 billion by 2025 and one trillion by 2035. And with the AI revolution only intensifying, we will see AI training and inference costs continue to rise. In turn, so will the demand for faster and stronger computing power.”
For QA and testing teams in financial services, especially those working with AI-enabled tools, model risk validation, and real-time systems, this transformation is already underway.
“Model sizes for large language models have grown from 94 million parameters in 2018 to the estimated more than one trillion for GPT-4,” Cook wrote.
“According to Nvidia, the processing power needed to train generative AI models is increasing 275 times every two years,” she added.
Quantum computing
Perhaps the most dramatic shift on the horizon is quantum computing. “Quantum computing could be one of the biggest revolutions yet,” stated Haim Israel, equity strategist at BofA Global Research. “It uses sub-atomic particles to store information and uses superpositions for complex calculations.”
The implications for software testing, security, and systems modeling are far-reaching. “Quantum computers can master almost every aspect of banking,” Israel noted. “That includes portfolio optimisation, data analytics in real time, pattern detection, encryption and fraud detection.”
“A human would have to perform one sum every second for around 50 quintillion years to equal what a quantum computer can do in a single second,” he wrote.
The analyst described quantum computing as “a technology that can perform endless complex calculations in zero-time, warp-speeding human knowledge and development.”
While full quantum advantage has not yet been achieved due to error-prone systems, BofA said the technology is approaching maturity, with timelines for broad utility estimated between 2030 and 2033.
“The challenge is to increase the qubit number and quality,” Cook noted, pointing to next-generation chips and improved qubit fidelity as critical developments.
She added that “the convergence of AI and quantum technologies can enable fundamental improvements in the physical world as well as the digital one.”
The report further pointed to high-performance computing (HPC) as a key technology for managing this scale and complexity.
“HPC systems can perform quadrillions of calculations per second, making them indispensable for AI model training, algorithmic trading, and real-time simulations,” explained Israel.
“These systems are more than one million times faster than today’s fastest desktops,” Cook said.
In fact, the latest TOP500 list, tracking the world’s most powerful supercomputers, shows aggregate HPC performance exceeding 7 exaflops, with the U.S. now leading global deployment.
“Accelerated systems account for over 70% of aggregate compute performance,” Israel said. “The maturation of Moore’s Law and serial computing is shifting more workloads to parallel computing”.
That shift is especially relevant for QA workflows that require simultaneous validation across multiple environments, such as containerized microservices or multi-region banking applications.
Edge computing
But it’s not merely about raw power, it’s also about where that power resides. Edge computing is emerging as a crucial architecture for latency-sensitive, distributed QA environments.
“Edge computing addresses latency, bandwidth, autonomy, and privacy requirements,” Cook said. “It helps meet the needs that centralised cloud can’t always address”.
This matters in financial services where fraud detection, biometric authentication, and customer interactions increasingly rely on AI inferencing at the edge.
“Unlike training, which will occur in core compute with hyperscalers, inferencing requires a distributed, scalable, low-latency, low-cost model,” noted Israel. “That is what the edge compute model provides.”
The report also points out that “CPUs are the optimal choice to support inference at the edge” while GPUs, despite their power, “consume a lot of power, resulting in costly cooling measures, and make hardware design complicated.”
The Bank of America report also explores spatial computing, blending real and virtual environments, as a longer-term disruptor in financial UX and testing.
“We are on the cusp of moving away from the traditional keyboard-and-mouse configuration,” said Cook. “Touch gestures, conversational AI, and AR/VR will enable more immersive and real-time interfaces.”
As she put it, “the fastest transformation in human history is ahead of us”, and for QA professionals working across financial services, it’s not just a matter of speed, but of preparing for an entirely new computational paradigm.
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