Mind the AI gap: 3 common artificial intelligence pitfalls and how finance firms can avoid them

Artificial intelligence (AI) promises to bring operational and strategic benefits to finance firms, improving decision-making, combating fraud, increasing efficiency and elevating customer service. Yet, scaling up and unlocking the full potential of any emerging technology can prove difficult. Drawing on lessons learned deploying technology solutions to leading players in the sector, Rob Smith, CTO of award-winning cloud services provider Creative ITC(https://www.creative-itc.com/), explains how finance organisations can avoid common AI minefields to achieve greater ROI.

As organisations progress digital transformation to gain a competitive edge, the inexorable rise of artificial intelligence (AI) and machine learning (ML) in the finance and banking sector shows no sign of slowing. Offering firms new ways to accelerate and improve decision-making and customer service, half of UK banks plan to invest more in these new technologies 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.


Expanding AI practice

The fastest AI adoption rates are being seen in middle office areas such as risk management, payment fraud and debt analysis, delivering efficiencies and financial savings. Credit evaluation processes are being automated, speeding up applications and improving loan decisions. AI and ML solutions are also helping organisations to minimise fraudulent financial transactions, flagging suspicious patterns to expedite necessary interventions. There’s also been success in optimising payment collections with AI, reducing payment delinquency rates.

AI is increasingly supporting human decision-making in investment banking, too. Identifying performance changes to enable better-timed trades, the technology is being used by asset and hedge fund managers to pick stocks and bonds.

As AI grows in maturity, institutions are building on existing solutions across their organisations. AI deployment in front office areas such as chatbots is increasing among larger players such as retail, rapidly resolving common enquiries 24/7.


Rob Smith

Hidden pitfalls of AI expansion

However, although many finance organisations are achieving successful results from these kind of AI deployments, scaling up enterprise-wide often remains elusive. For many finance firms, AI expansion often exposes underlying problems. AI projects can still over-run, overspend and fall short in terms of results. The most common constraints are:

  • Legacy infrastructure limitations

Huge AI processing requirements exhaust data centre and network capacity, causing latency issues or even outages. Trying to share actionable 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. All too often, this can lead to poor user experiences and collaboration challenges.

  • Internal resource and expertise gaps

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.

  • Unplanned expense

Many IT budgets are being stretched to accommodate new technologies. Of course, 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.


Increasing your chances of AI success

To satisfy these extra IT infrastructure requirements, finance firms are increasingly moving to the cloud. Many companies use a combination of cloud and on-premise platforms to give them the agility and scalability they need for high 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.

With the added pressure AI brings to in-house teams, many organisations quickly realise that trying to achieve this perfect mix of infrastructure, resources and skills on-site is simply not feasible.

Infrastructure-as-a-Service (IaaS) removes those problems 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 overcome legacy issues, while offloading hardware costs, upgrade burdens and skilled resourcing requirements to a managed service provider (MSP). This 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.


Selecting the right MSP

Having decided to explore the IaaS route, rigorously check the MSP’s technical credentials. Here are five key questions to ask:

  1. Do they have a strong track record in finance and can help you meet industry and regulatory requirements?
  2. Can you retain necessary data and workloads on-site, while accessing the latest technologies across public, private and hybrid cloud environments?
  3. Can they evidence expertise to deploy the right IaaS solution with ongoing management, optimisation and UK-based 24/7 support?
  4. Do they have expertise in high-performance graphics processing units (GPUs) capable of handling vast and complex workloads simultaneously, which are essential for rapid AI and real-time business analysis?
  5. Do they have end-to-end expertise from devices, connectivity and cloud to storage, security and UX?

IaaS solutions are empowering organisations with more effective handling of complex AI workloads and headache-free management. In today’s resource-strapped environment, this route is helping finance firms stay ahead of the competition, providing new flexibility, speed and scalability on a realistic budget and time frame. The leading finance firms are increasingly leveraging as-a-Service models to unlock greater ROI from new technologies. This trend is accelerating digital transformation across the sector and enabling IT leaders to unlock greater strategic benefit.

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