How eCommerce and fintech firms are leveraging the power of AI-enabled data centres

The fintech and eCommerce sectors are booming. By 2026, the global eCommerce market is expected to be worth more than $8.1 trillion annually. Meanwhile, a recent study by McKinsey revealed there are now more than 272 fintech unicorns, with a combined market cap of $936 billion – a sevenfold increase in just five years. 

Technologies such as artificial intelligence (AI), blockchain and process automation remain critical to sustaining this growth, with companies such as NVIDIA turning to GPU-powered servers to overcome the limitations of conventional data centre infrastructure.

Designed originally to accelerate computer graphics and image processing, GPUs perform complex three-dimensional vector calculations, enabling them to perform multiple operations simultaneously.

Making sense of vast amounts of data

This ability to multitask on a huge scale makes GPUs an ideal tool for managing and analysing vast volumes of data. For example, GPUs can process neural network training data up to 250 times faster than conventional CPUs. They can also do the job more accurately, making them particularly well suited to high-quality data-driven decision-making. 

For example, credit specialist Capital One uses a suite of GPU-optimised data libraries to accelerate its data science and analytics pipelines. The firm has not only achieved a 100-fold increase in data model training times, it has also reduced its costs by nearly 98%. 

This uptick in processing power means forward-thinking firms now have an opportunity to explore data models faster and with greater confidence. They can also do so in a more cost-effective and energy-efficient manner, and with a faster time to ROI. 

Automating and improving processes in financial services

Financial services firms are among those with the most to gain from the huge potential of GPU-powered AI, and many companies are already leveraging this technology to automate and improve mission-critical processes. These use cases include.

Algorithmic trading: analysing historical market and stock data to generate investment strategies, build portfolios and automatically buy and sell investments. Established banks which have developed algorithmic trading strategies include BMP Paribas, Deutsche Bank and Credit Suisse.

Detecting fraud: combatting the most sophisticated types of transaction and identity fraud, increasing fraud detection accuracy, and boosting anti-money laundering and know-your-customer regulation. American Express, BNY Mellon and PayPal are already using a form of AI called natural language processing (NLP) to detect and prevent financial fraud. 

Accelerating payments: Fintech payment firms such as PayPal are using machine learning to improve payment authorization rates on their platforms. One way it does this is by predicting and efficiently managing instances where a bank could decline a payment. 

Achieving competitive advantage in eCommerce

eCommerce companies are also waking up to the power of AI, using the technology in innovative ways to achieve competitive advantage. Among some of the innovative use cases in the eCommerce sector include: 
Predicting and managing customer churn: AI can analyse customer behaviour and identify which customers are most likely to make repeat purchases This enables sales and marketing teams to target and better allocate resources to more profitable customers.

Dynamic pricing: eCommerce giants such as Amazon and eBay are long-term advocates of AI-powered dynamic pricing. They use the technology to analyse market demand, competitor pricing and other factors so they can adjust their prices in response. 

Enabling visual and voice search techniques: Retailers such as ASOS, Forever 21, and Home Depot are using AI to free their customers from their keyboards, find the products they really want and accelerate their path to purchase.
It's interesting to note that financial services and eCommerce, as well as many other sectors, are exploring how AI can drastically improve online customer interactions. Thanks to generative AI (GenAI), clunky chatbots will soon be a thing of the past. Instead, GenAI-powered solutions are particularly good at finding the best answers to customer questions and sharing that information in a human-like way. Deutsche Bank, American Express and Wells Fargo are among the banks that are starting to go live with such GenAI-powered solutions. 

Using AI to optimise the data centre

There is clearly a huge scope for using GPU-powered AI to improve products and services within the eCommerce and financial services sectors, but the benefits don't end there. The technology is also transforming how data centres critical to these sectors are managed and optimized, how uptime is ensured, and how higher levels of sustainability are achieved.

AI is not only helping data centre managers identify, troubleshoot and mitigate outages in a reactive way, but it is also automating this process, predicting faults and triggering self-healing mechanisms. For instance, AI can be trained to identify unusually slow traffic within a particular node and then re-boot a process or the entire node to fix the issue. Other tasks such as energy management (including cooling and power management), inventory management and systems update management can also be automated in a similar way using AI.

AI is also being used to boost data centre visibility and decision making, helping managers to identify opportunities to optimize resource allocation and improve both workload management and capacity planning. Such processes can reveal golden opportunities to right-size data centre infrastructure, cut power consumption and reduce environmental impact.

Research by Schroders suggests that AI-related data centre power consumption is likely to increase sevenfold to 7GW by 2026. However, this spike in energy consumption and associated carbon emissions can be reduced, at least in part, through the careful use of GPUs. That's because GPUs are more powerful, fewer servers are needed, and data centre physical footprint and cooling requirements are reduced.

Simplifying and accelerating financial transactions with blockchain

Blockchain is another GPU-reliant technology helping to disrupt both the financial services and eCommerce sectors. Fintech firms such as OpenZeppelin are harnessing the power of smart contracts to simplify complex financial transactions. Smart contracts, which are powered by blockchain, automatically execute when certain conditions are met. They remove layers of intermediaries, reduce cost, and speed up contract execution. For example, a smart contract can be programmed to make a payment when a product or service has been successfully delivered.

Meanwhile, Ripple, one of the best-known blockchain-based payment systems, enables banks, corporations and crypto exchanges to transfer money without the need for a third-party processor. The firm's solution has made cross-border payments significantly easier, faster, cheaper and more secure. 

The data centre as the backbone of innovation

As transformative technologies such as AI and blockchain continue to evolve and become more integral to the success of the fintech and eCommerce sectors, the role of data centres becomes increasingly critical. 
Data centres equipped with GPUs deliver the necessary computational power that AI applications require. As we continue to push the boundaries of what AI can achieve, the role of data centres will only grow in importance. They are not just a supporting infrastructure but a vital component in the journey towards a more AI-driven future.