Top 5 ways that the finance industry can prepare for AI

The rise of artificial intelligence is inevitably going to have a disruptive effect on businesses across every sector, but further transformation is expected

Despite already been central to many of the sector's processes, research suggests that many industry players are still not in a position to fully integrate AI into their business
Despite already been central to many of the sector's processes, research suggests that many industry players are still not in a position to fully integrate AI into their business 

Artificial intelligence (AI) is set to revolutionise every industry, and the finance sector is no exception. AI will make businesses faster and cheaper to operate, creating new opportunities and adding an additional estimated $13trn to global economic activity by 2030.

Financial institutions are increasingly looking to AI to aid further operations

Despite the rise of AI, Digital Realty’s latest research has shown that over a third of IT decision makers in the UK’s largest financial services companies are not ready to implement the technology into their business. Elsewhere, the figures are similarly high. For instance, they are calculated at 27 percent in Ireland, 18 percent Germany and 23 percent in the Netherlands.

Processes that are already seen as the norm in the financial services industry, such as fraud detection and stock trading, are made possible by AI, and financial institutions are increasingly looking to AI to aid further operations. These include customer communication, predictive analytics, trade processing, and intelligent investment solutions. Listed below are the top five ways your business can prepare for the surge in AI.


1 – Identify key areas that would benefit from AI
Before taking the first step to introduce AI into your business, it is crucial to review and evaluate existing processes to determine which existing processes can and should be automated to free up time for employees to focus on higher-value tasks. It is important to hold discussions with your workforce to identify the processes that are repetitive and tedious, and those that can be carried out with automated methods.

In the financial services industry, tailored customer service, risk model improvements, and day-to-day transactions have been made possible by AI; firms in the sector should continue to iteratively evaluate their processes so that AI can be implemented to maximise process efficiency across the business.


2 – Educate your workforce
It is important to involve the workforce in the initial planning stages of AI implementation for the reasons laid out above. However, it is often recognised that automated processes, such as AI, can be seen as a threat by employees with regards to being replaced and losing their job. Whilst this may be true to some extent, this can be mitigated if they are educated on how AI can, and will, be introduced in the near future, and how it should not be seen as their replacement, but rather, should be welcomed as it will free up time to focus on other key activities.

In the case of the finance industry, employers should reiterate the fact that the more mundane tasks, or tasks that require uninterrupted manpower, such as around-the-clock monitoring for security attacks, are not the most valuable use of time. It is equally important to instil a culture that promotes a harmonious relationship between colleagues and AI to ensure that change is accepted.


3 – Upgrade your infrastructure
The rise in AI applications will bring about a host of new demands for data.  Complex data processing is required to ensure that businesses welcome AI with functioning arms. This shift can be costly if outdated infrastructure must be upgraded to the standard required to facilitate AI operations.

Outsourcing providers, on the other hand, build their infrastructure with the focus on interconnection – they are constantly redesigning their infrastructure to evolve with new technologies, so businesses can benefit from a purpose-built environment without having to worry about costly ongoing updates to their own infrastructure.

Upgrading infrastructure introduces a host of benefits, such as lowered operation costs, scalability, increased security, a centralised integration platform, and improved functionality. These benefits will further assist in streamlining operations, such as analysing large amounts of data, customer support, and real-time data transaction views, to name a few. Companies, especially those in trading or stockbroking, benefit from faster and better service for customers, together with efficient end-to-end data flows.


4 – Set out a clear AI strategy
As with anything, financial services firms need a clear AI implementation strategy from the outset to ensure that whilst AI is being developed and incorporated into processes, there is a clear deployment strategy, which includes a rollout plan for key stakeholders, like customers.

A well-planned strategy is vital in ensuring that the incorporation of AI delivers optimum benefits for the business, such as better-tailored and more accurate services for customers at a lower cost, as well as enhanced prevention of criminal activities and improved detection of fraud and money laundering.


5 – Look at other firms’ strategies
It is important to determine parallels in the AI-led systems with other companies; and learn from the mistakes of others! In order to ensure that your company is keeping abreast of its competitors, and maintaining their competitive edge, financial services companies should look not only to their direct competitors for learnings, but beyond the industry.

AI is a versatile and powerful technology but is not without its teething problems. With regards to the financial services industry, previous encounters with AI have resulted in biased consumer targeting. When looking to adopt AI, it is wise to look at previous mishaps to ensure that future processes are developed which incorporate best practices.

To fully embrace the benefits of AI, companies will need to meet new processing and interconnectivity demands. The challenge is forcing them to look to cloud and data centre partners for the purpose-built infrastructure, rapid low-cost interconnection, and simple management of these complex data environments that can underpin their AI ambitions.