The Top Benefits and Challenges of AI Adoption in the Financial Sector
The emergence of AI has had a positive impact on the financial industry and has enhanced productivity, in particular in the accounting and banking areas. Therefore I anticipate that machine learning will definitely be a significant area of investment in the near future for this sector. However, as with any change of this magnitude, the […]
The emergence of AI has had a positive impact on the financial industry and has enhanced productivity, in particular in the accounting and banking areas. Therefore I anticipate that machine learning will definitely be a significant area of investment in the near future for this sector. However, as with any change of this magnitude, the benefits offered by the implementation of AI in the financial sector are met with a number of challenges – most notably businesses ensuring they are equipped with the right technology, staff and skills to embrace AI and automation.
Automation is now used to perform or enhance many administrative tasks, and Artificial Intelligence is already more a part of daily life than you might realise. Robotics, while commonplace in manufacturing, are beginning to show impact in other sectors. One of the key drivers behind the adoption of AI software in the financial sector is the time-saving benefits it offers users. Gone will be the days of long hours spent working on spreadsheets, processing data, or handling customer enquiries. Those tasks will be streamlined by machines, allowing workers to focus more time on complex tasks which require human touch. As well as working with advancing technologies, junior employees will be involved in more planning, reporting and analytical jobs, and as such their required skill set will change.
Gone will be the days of long hours spent working on spreadsheets, processing data, or handling customer enquiries.
Through machine learning, artificial intelligence can painlessly consume and process large amounts of data at an accelerated level. Its vast speed brings efficiency and productivity to the financial sector, and as it continues to develop and become even more efficient, it can identify more patterns than ever before, providing scope for customised offerings to customers. However, this being said, adoption of AI in the financial sector imposes many challenges to the industry. The use of AI’s ability to consume large amounts of consumer data raises questions about how this information is stored and processed and to what end. Organisations that encourage, and even mandate the uptake of these types of technologies must tread carefully. Individuals are already highly attuned to the sensitivity of their personal information and will require robust guarantees about the security of any further information they are willing to give up.
One limitation of machine learning in this context is that it primarily relies on the basis of historical data sets and as a result, can fall into the trap of becoming repetitive, as well as potentially giving way to conscious or unconscious bias. For instance, how fair can a financial system really be without human involvement? In a world where new technologies are quickly improving or even replacing existing processes, there is one area that cannot be automated, and that’s building strong relationships with clients. The human element is needed in these instances to perform certain job functions that AI is incapable of replicating. Individuals have the ability to be aware of their own emotions and those of others, but also their capability of showing empathy in the way they handle interpersonal relationships, which is known as emotional intelligence.
It’s crucial for businesses, in the fast pace of today’s world, to continue to develop and think about where their use of data can get them tomorrow, as well as where it’s got them today. Organisations must not become complacent, and instead continue to reflect on their processes, challenge routine and be future-facing in their approach to machine learning.
One limitation of machine learning in this context is that it primarily relies on the basis of historical data sets and as a result, can fall into the trap of becoming repetitive, as well as potentially giving way to conscious or unconscious bias.
Over the last two decades, technology has advanced at such a speed that many roles in the financial sector have either disappeared or wholly changed due to the implementation of AI technology. One of the many challenges facing the finance industry is the impact that AI is having on the job roles within sector. Artificial intelligence and automation can take on many of the tasks a transaction led accountant or data administrator would typically undertake, with little or no human involvement. The process is almost seamless, error-free and time efficient.
The challenge of economic survival of the financial sector is to not only accept these changes, but to capitalise on them. With any significant change in the market, there’s always a fear that it will eliminate jobs from the workforce. AI tools may well remove a number of job tasks carried out by accountants and data administrators, but rather than eradicating jobs and losing talented members of staff, employers will need to ensure that their HR directors are equipped to spot the right skilled professionals who are well versed with the latest AI technology. The HR function will also need to quell fears of job losses amongst employees and instead empower their staff to adapt and develop new skills to work alongside new technologies.
At this juncture, skilled employees are key – and we anticipate a change in the skills that businesses across the financial sector will be demanding from their employees and prospective hires. For years, Michael Page clients in this sector have been seeking candidates with financing and analysis skills; those that have a strong understanding of financial planning and reporting; people who are adept at using Excel and other such software. Our recently launched Skills Checker tool has taken the most in-demand skills for roles across the financial industry to highlight what employers are looking for today. But as we continue to see AI and automation adoption increase in the sector, we expect to see a rise in employers expanding the skill sets they require from new employees with coding and AI experience becoming ever more valuable.
One of the many challenges facing the finance industry is the impact that AI is having on the job roles within sector.
To the same end, the advent of these new technologies presents the opportunity for businesses to enhance their current workforce by equipping employees with the skills to work alongside AI and automation. A challenge in itself, such training should not be brushed off as a ‘nice to have’; it is vital for the growth, and even the survival, of a business. Employees are the lifeblood of any business, as the landscape of the financial sector changes, businesses must ensure that their workforce is keeping pace with the industry.
Before incorporating AI software into their businesses, organisations will need to think strategically about what their key objectives are and what they hope to achieve from using the technology. This is the only way they can truly expect to see any long-term benefits, through a strategic and considered approach – not simply thinking of AI as a ‘nice add-on’. It’s also important for organisations to have realistic expectations.
Businesses should start by looking at key areas where they can make an impact by using this technology on more routine tasks and go from there. This will help to build their confidence and understanding of the software over time, rather than trying to implement it all at once. Strategic thinking and patience are key here.
Although robots and AI will inevitably take a lot of the more data-driven job functions, there will be a change in how humans and machines interoperate for the highest level of efficiency and playing to each other’s strengths. The increased use of AI in the financial sector is going to spur on new innovation, and an entirely new landscape of jobs are going to emerge. Although there is always a lag between the adoption of new jobs and loss of current jobs, up-skilling and re-skilling are going to be the key to success in the future of the financial job market.