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Releasing the full potential of new technologies can be difficult. So, although finance firms are achieving success from artificial intelligence (AI), scaling up enterprise-wide remains elusive for many. Rob Smith, CTO of award-winning cloud services provider Creative ITC, explains how the growing trend of as-a-Service IT models is accelerating digital transformation across the finance sector and enabling IT leaders to unlock greater ROI.

Uptake of artificial intelligence (AI) and machine learning (ML) in the banking and finance sector continues to grow as organisations seek competitive advantage. These new technologies promise significant benefits, offering new ways to improve decision-making, boost efficiency, fight fraud and enhance customer service. Following the pandemic, half of UK banks plan to invest more in these  technologies. By 2030, global AI annual expenditure on AI by banks and finance firms is predicted to reach $64.03 billion.

Growing appetite for AI

In finance, middle office departments are increasingly adopting AI to deliver efficiencies and financial savings in areas like risk management, payment fraud and debt analysis. Automated credit evaluation processes are expediting applications and improving loan decisions. AI and ML solutions are flagging suspicious patterns to help organisations to minimise fraudulent financial transactions. Some firms are optimising payment collections with AI, reducing payment delinquency rates.

AI in investment banking is supporting human decision-making, too. The technology is being used by asset and hedge fund managers to identify performance changes and enable better-timed trades.

As AI matures, companies are looking to expand existing solutions across their entire organisations. AI deployment in front office areas such as chatbots is increasing among larger players such as retail banks to rapidly resolve common customer enquiries 24/7.

Common AI challenges

Sadly, organisations seeking to unlock greater potential from AI all too often hit obstacles. Many AI projects still over-run, overspend and fall short in terms of results and attempts to scale up enterprise-wide often expose underlying problems. The most common constraints are:

  • Limitations of legacy infrastructure

Many AI deployments result in poor user experiences and collaboration issues. Huge AI processing requirements overwhelm data centre and network capacity, causing latency issues and outages. Attempting to share insights with stakeholders in multiple locations can reveal further weaknesses in legacy infrastructures, which haven’t been designed to share such datasets securely at speed and scale. 

  • In-house skillsets

Specialist IT skills are required to optimise AI workloads and enable an organisation to realise its full business benefits. Most finance firms aren’t able to employ and retain large, multi-skilled IT teams, or devote adequate resources to ensure long-term AI success.

  • Unrealistic business case

Total cost of ownership (TCO) doesn’t stop with acquiring the AI solution itself; it also includes implementing and maintaining the right IT infrastructure and integration systems to support long-term AI deployment.

Overcoming AI obstacles

Many organisations quickly realise that trying to achieve this perfect mix of infrastructure, resources and skills on-site is simply not feasible. Finance firms are increasingly moving to the cloud, using a combination of cloud and on-premise platforms to give them the agility and scalability they need for high AI loads, without the need to own and maintain massive unused capabilities during quieter times. Research shows that the businesses enjoying the biggest gains from AI are taking more advantage of cloud infrastructure than their peers.

Infrastructure-as-a-Service (IaaS) overcomes legacy challenges and provides a cost-effective foundation for AI growth. Providing on-demand access to computing power and storage via the cloud, it allows firms to offload hardware costs, upgrade burdens and skilled resourcing requirements to a managed service provider (MSP). This quickly delivers savings on data centre space, infrastructure, licensing, support, training and headcount, providing a fully-managed service in a predictable, monthly OpEx model.

Futureproofing your AI growth strategy

Look for a provider with a strong track record in finance who will help you meet industry and regulatory requirements. You should be free to choose which data and workloads to retain on-premise, while accessing the latest technologies across public, private and hybrid cloud environments, delivered as a fully managed, seamless solution. Make sure you’ll benefit from access to the latest technologies and regular updates, rather than having to invest in expensive refreshes during the contract.

Be confident they can offer the right IaaS solution with ongoing management, optimisation and UK-based 24/7 support. Take a close look at their technical credentials and check their expertise involving advanced graphics processing units (GPUs) capable of handling vast and complex workloads simultaneously. This will be essential to high-performance, hyperscaled computing for rapid AI and real-time business analysis.

Behind the impressive AI growth headlines, there’s widening gap between those finance organisations leading the way in digital transformation and the firms who are striving to keep pace. The leading financial institutions are driving a shift towards as-a-Service IT models across the sector to enjoy greater return on their technology investment. IaaS is empowering more effective handling of growing AI workloads to help finance firms stay ahead of the competition. Given the clear competitive advantages, now is the time for finance companies to access the scalable solution they need to unlock greater operational and strategic benefit.

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