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The tip of the AI-ceberg: How finance firms can unlock greater value from artificial intelligence

Finance organisations are increasingly turning to artificial intelligence in pursuit of competitive advantage. However, although many firms are achieving successful results from AI projects, scaling up enterprise-wide often remains elusive. 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) is continuing to rise as financial organisations progress their digital transformation plans. These new technologies offer banking and finance firms new ways to accelerate and improve decision-making and customer service. No wonder then that half of UK banks plan to invest more in them as a result of the pandemic, and global annual spending on AI by banks and finance firms is predicted to reach $64.03 billion by 2030.

Growing AI adoption rates are driving greater operational efficiencies and financial savings, particularly in in middle office areas such as risk management, payment fraud and debt analysis. Institutions can now automate their credit evaluation processes, speeding up applications and resulting in better commercial loan decisions. With consumer debt predicted to rise, Mastercard company Brighterion has optimised payment collections with AI, reducing delinquency rates by 76%.  AI and ML deployments are also helping firms to drive down fraudulent financial transactions, flagging suspicious patterns to expedite necessary interventions.

In investment banking too, AI is increasingly supporting human decision-making. Asset and hedge fund managers are using the technology to pick stocks and bonds, with AI identifying performance changes and rapidly interpreting breaking news to enable better-timed trades. This ensures they can quickly capitalise on upward and downward shifts.

As AI gathers momentum, institutions are highly aware of its greater potential and are developing existing solutions to solve more complex challenges. Larger players such as retail banks are looking to expand their deployment of AI across their organisation, meaning front office AI usage in areas such as chatbots is becoming more prominent. Natural language processing and machine learning are being used to support chatbots, rapidly resolving basic and common enquiries 24/7 and only referring complex enquiries to humans.

Mind the gap

Delving behind the impressive AI growth headlines soon reveals a widening gap between those finance organisations leading the digital transformation charge and the firms who are clinging to their coat tails trying to keep pace.

As you’d expect, IT budgets are a key factor in AI success. Three quarters of financial services professionals at banks with over $100 billion in assets are currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets. Larger investments are enabling the leading players to unlock greater benefits from AI to stay ahead of the competition. 45% of the organisations seeing the greatest operational advantages refresh their AI stack at least annually to benefit from the latest technologies. This level of continued investment may be beyond the reach of many financial firms.

Legacy limitations

Recognising the potential of AI and unlocking wider benefits are two different things. For many finance firms, scaling up from successful AI projects to an enterprise-wide level remains elusive and often reveals underlying problems.

Legacy infrastructure is frequently the most severe limitation. Huge AI processing requirements can easily overload data centre and network capacity, causing latency issues or even outages. Many IT leaders find themselves struggling to derive the timely analysis and deliver the actionable insights their organisations need.

Failing to overcome these challenges can lead to poor user experiences and sub-optimal collaboration. Once an AI platform has performed its magic, the company will wish to share actionable insights with stakeholders, some of whom may be working remotely or in globally dispersed offices. This can further expose weak points in legacy infrastructures, which haven’t been designed to share such valuable assets and huge datasets securely at speed and scale.

Investment in AI doesn’t end with acquiring the solution itself, and many IT budgets are being stretched to accommodate new technologies. Total cost of ownership also includes implementing and maintaining the right IT infrastructure and integration systems to support AI deployment in the long-term.

Organisations often struggle with insufficient in-house resources, too. Many finance firms don’t have the luxury of employing extensive multi-skilled IT teams, with the specialist skillsets required to optimise AI workloads and enable an organisation to realise the full business benefits.

Overcoming obstacles

Meeting extra data centre and network requirements for effective AI use ideally means having the capacity and flexibility to process high data volumes when needed, while avoiding the need to own and maintain massive unused on-premise resources throughout quiet periods. For most finance firms, achieving this cost-equation balance is unrealistic.

Increasing cloud use is one route enabling businesses to escape the burdens of expensive IT infrastructure upgrades and access the latest technologies. Many companies use a combination of cloud and on-premise platforms to give them the agility and scalability they need for both their overall IT and AI workloads. Interestingly, research shows that the companies enjoying the biggest gains from AI are taking more advantage of cloud infrastructure than their peers. The best performing businesses deploy two thirds (64%) of their AI workloads in public or hybrid cloud, compared with 44% at other companies.

Many leading players in the finance sector are turning to Infrastructure-as-a-Service (IaaS) to gain on-demand access to cloud-based systems and specialist skills for successful AI deployment. This gives them newfound agility, while offloading the burden of hardware costs and upgrade burdens to a managed service provider (MSP). A specialist MSP will boost in-house resources, taking away the headache of designing, implementing, managing and optimising IT infrastructure and AI systems. The MSP route quickly pays back with 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 investment

When transitioning to the cloud to boost AI capabilities, remember not all clouds or cloud services providers are the same. Scrutinise the fine print and make sure you’ll benefit from access to the latest technologies and regular updates, rather than having to invest in expensive upgrades during the contract.

Seek out a provider with a strong track record in finance who will offer a tailored, fully managed solution to meet industry and regulatory requirements, allowing you to retain data and workloads on-premise, while accessing the latest technologies across public, private and hybrid cloud environments.

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

As AI deployment in banking and finance grows, demand for extra infrastructure capacity is increasing and cloud adoption rates continue to rise. The leading financial organisations are driving a shift towards as-a-Service IT models across the sector in order to derive greater return on their investment in these new technologies.

IaaS is empowering ever more effective handling of complex and shifting AI workloads, with simple, stress-free management. The result is invaluable flexibility and speed on a realistic budget and time frame, enabling scalable and reliable problem-solving 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 infrastructure they need to unlock greater operational and strategic benefit.

Global Banking & Finance Review


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