Becky Glover shares her experience, insight and invaluable knowledge about whether finance professionals should embrace or resist artificial intelligence (AI).
Firstly, what is AI? It’s talked about a lot, but do we really understand what it is?
The term ‘AI’ has been around since the 1950s, but it feels like everyone’s only been talking about it in the last few years. Today, AI is all about Large Language Models (LLM), such as ChatGPT, and machine learning, such as Xero’s ability to reconcile bank transactions.
“Artificial intelligence is a machine’s ability to perform some cognitive functions we usually associate with human minds.“
AI is revolutionising industries across the globe, and the finance sector is no exception. AI can help with everything from accounts payable to generating responses for customer emails. AI has become an integral part of the modern finance sector. However, this rapid integration raises a critical question: Should we embrace or resist AI in finance?
Should we embrace AI in finance?
Efficiency and speed
One of the most compelling arguments for embracing AI in finance is the significant increase in efficiency and speed it offers. AI algorithms can process vast amounts of data in seconds, far outpacing us mere humans! This speed is crucial in any role where data and results are needed as quickly as possible. In a fast-paced world, who doesn’t want their financial results quicker to be able to make faster and better business decisions?
Software providers such as iplicit, Xero, and Sage suggest that their intelligent software can save finance teams days every month. AI is removing mundane tasks, such as coding and processing invoicing, and freeing up professionals for more value-added work.
Enhanced accuracy and reduced human error
Humans are prone to errors, especially in high-stress environments. AI systems, on the other hand, can operate with high precision. By analysing and learning from historical data, trends and various financial indicators, AI and machine learning can make more accurate predictions and decisions. This capability reduces the likelihood of costly mistakes and can lead to more consistent and accurate financial information.
Risk management and fraud detection
AI’s ability to analyse data quickly and accurately makes it a great tool for risk reduction and fraud detection. Machine learning algorithms can identify patterns and anomalies that might indicate fraudulent activities. This is currently being utilised by finance departments to flag duplicate invoices, spot duplicate bank details, check bank information against company names, and more.
Deepfakes are another threat that can be tackled by investing in the right training and technology. Recently, a finance employee was tricked into paying $25 million after a Microsoft Teams call with, what he thought was, his CFO and wider team. In fact, this was technology copying everything – from their voices to facial expressions!
As Generative AI (GenAI) becomes even more sophisticated and creates deepfakes of senior staff to defraud companies, technology will be vital to combat this.
The case for resisting AI in finance
"It’ll take all the jobs!"
One of the primary concerns with using AI in any sector is job losses. As AI systems take over tasks that were previously performed by humans, jobs are indeed at risk.
However, a recent report from PwC showed that if professionals can embrace technology, they could expect pay increases of up to 25%.
Ethical and bias issues
AI systems are only as good as the data they are trained on. This means that if the underlying data is biased or flawed, the AI’s decisions will reflect those biases. Ensuring that AI systems are transparent, fair, and accountable is a significant challenge that the industry as a whole must address.
Security and privacy concerns
The integration of AI in finance teams also raises significant security and privacy concerns. All businesses are targets for cyberattacks, and all technology systems, if not properly secured, could become a huge vulnerability. Moreover, AI uses and collects a significant amount of data – which raises questions about user privacy and data protection.
Striking a balance between leveraging data for AI and safeguarding user information is crucial.
Dependence on technology
Over-reliance on AI systems can also be detrimental. Finance requires judgement, and this sort of decision-making can only be made by a human who can take into account the external factors that AI may not fully understand or anticipate. In cases where AI systems fail or produce hallucinations, the consequences could be catastrophic.
Striking a balance
Given the compelling arguments on both sides, the question of whether to embrace or resist AI in finance is not straightforward. Instead of a binary choice, the focus should be on striking a balance that maximises the benefits of AI while mitigating its risks.
Regulation and oversight
Effective regulation and oversight are essential to ensure that AI is used responsibly in all businesses, especially in finance. Regulatory bodies must establish clear guidelines for developing and deploying AI systems, focusing on transparency, fairness, and accountability.
Investment and upskilling of employees
While AI can automate many tasks, the need for human expertise remains. All businesses should be investing in training and upskilling their workforce to work alongside AI systems. In a world of fast-paced technology changes, training will no longer be a short course a few times a year. Training the workforce needs to be a main focus, with this being delivered regularly and as soon as new technologies are released or updated.
To recruit and retain the very best, a business must embrace the continuous learning of its workforce.
Final thoughts
The integration of AI in finance is inevitable, and already here! It brings a lot of benefits and challenges. Embracing AI can lead to greater efficiency, accuracy, and innovation. However, it also raises significant concerns.
My personal opinion is that we should lean into technology and learn how to use it to its full potential. By doing so, we free ourselves from mundane tasks, allow greater purpose in our roles, and earn more money too. However, we do need to be conscious of the security issues. As well as protecting us, it could open us up to new risks. Continuous learning is crucial to understanding both the benefits and negatives of emerging technology, and I urge everyone to give it a go!
Upskill today
All of our data and technology apprenticeship programmes come with a complimentary one-day course and micro-credential in Generative AI. If you’re interested in upskilling with data skills and AI in your current role, read more on how to speak to your current employer about starting an apprenticeship.
Keep the conversation going and reach out to Becky Glover via email at hello@beckyglover.co.uk. You can also find her on LinkedIn or via her website.