The release of ChatGPT early last year put the spotlight on the rapid advancement of Artificial Intelligence (AI), sending ripples through almost every industry. Other large language models (LLMs) such as Google’s Bard, Microsoft’s Bing and Anthropic’s Claude followed shortly after, and the uptake was immediate. ChatGPT soon became the fastest-growing consumer app in history, according to research by UBS – and with its ability to do everything from write articles and poems to help create webpages and code, it’s not hard to see what created the buzz.
It’s just one of a string of recent AI advancements, of course. In recent years, we’ve seen everything from Siri and Alexa to robotic assembly lines, self-driving cars and Sophia the humanoid crop up, revolutionising a whole plethora of sectors – not least finance.
From data analysis to algorithmic trading, task automation to fraud detection and risk management to regulatory oversight, AI applications in finance are nigh-on endless, bringing a slew of changes to the industry. We spoke to Alejandro Reynoso – Co-Founder of DEFI Capital, a next generation investment bank and advisory firm focused on AI and blockchain, and External Lecturer in Algorithmic Trading and former Research Fellow at Cambridge Judge to find out what those changes are, which areas are being revolutionised, and the key opportunities to look out for now and in the future.
Data analysis
One of AI’s biggest strengths is its ability to process vast datasets at speed – and pick up on complex patterns beneath them, according to Reynoso. “AI often excels in processing extensive datasets, uncovering intricate patterns and trends often eluding human analysts,” he says. “This ability enhances decision-making and substantially elevates productivity. Decades of digital financial services have generated vast, well-structured datasets that continue to expand exponentially, so the financial sector is well-primed for this transformation.”
Trading, asset allocation and investing
That ability to analyse data and make informed decisions at speed has a myriad of applications – not least in trading. “Highly advanced AI robots can augment the capabilities of existing algorithmic trading platforms,” says Reynoso. AI stock traders are able to monitor real-time data and identify trends and anomalies in the market to execute trades and maximise returns. Natural language processing (NLP) tools can even analyse social media data and news sentiment to gain an insight into public perception and the resulting impact on a company’s stock.
AI can facilitate new investment strategies, some of which were previously considered too intricate to conceptualise. By integrating human interaction platforms with these potent robots, both retail and institutional clients can gain access to unprecedented speed, precision and sophistication – a realm once limited to only a select few industry leaders.
Relying on AI for those decisions obviously bring risks; algorithmic traders were behind the ‘flash crash’ of 2010, when nearly $1trn in market value was wiped off the New York Stock Exchange before being swiftly recovered. Risk management mechanisms are crucial in mitigating potential losses.
Risk management and fraud detection
Detecting and managing those risks is something AI itself is primed to do, however. “Machine learning algorithms can swiftly analyse massive datasets to pinpoint potential risks,” says Reynoso. “This enables proactive mitigation strategies and more informed decision-making.”
Being able to identify potential risks can also help financial firms detect suspicious or fraudulent activity, from transactions from new accounts to changes in behaviour; many AI tools are already doing this.
“AI will fortify security measures by enhancing fraud detection and prevention mechanisms,” says Reynoso. “Machine learning algorithms can scrutinise transaction patterns and swiftly identify suspicious activities, thereby safeguarding financial institutions and their clients from fraudulent actions.”
Regulatory compliance
It’s not just a case of detecting suspicious or fraudulent transactions; AI can also help compliance officers detect and react to anomalies at speed, ensuring financial institutions are complying to often complex regulations – and speeding up what’s traditionally been a manual, time-consuming process. “AI-powered systems will play a pivotal role in streamlining compliance processes and ensuring adherence to ever-evolving financial regulations,” says Reynoso. “These systems can flag potential breaches and contribute to maintaining the highest standards of regulatory compliance.”
Customer service and personalisation
Personalisation is another key opportunity for AI in the finance industry; algorithms can analyse transaction history to understand customer preferences and spending habits, and suggest financial products in line with them.
Financial institutions are catching on to the opportunities; in March, Visa partnered with AI and big data company Crayon Data to enable credit card issuers to gain insights into their customers’ behaviour and tailor solutions accordingly, using algorithms and machine learning.
“AI-driven chatbots and virtual assistants are also evolving to provide increasingly personalised financial advice and support,” says Reynoso. “They will offer clients round-the-clock assistance, addressing inquiries and facilitating transactions in real-time.”
Fintech revolution and the future
Reynoso believes all of this is likely to see fintech take over on a larger scale. “The convergence of Blockchain, Web3 and AI will catapult the decentralised finance space to unparalleled heights,” he says. “The financial services sector is currently experiencing the dual forces of innovation. Blockchain has already redefined payment systems, credit, insurance and crowdfunding.
“When combined with the emergence of digital financial communities and the power of AI, we may witness a rapid shift from traditional financial markets to a new era propelled by decentralised digital communities – possibly even faster and more profound than initially anticipated.”
Working with humans
So what does all of this mean for the future of humans in the finance industry? “It’s imperative to debunk a common misconception: AI’s primary goal isn’t to eclipse human capabilities or render them obsolete,” says Reynoso.
AI's purpose is to act as a catalyst, augmenting human creativity and potential. This union of man and machine should be anchored in principles that ensure the good of all. Honesty, transparency, and integrity aren't just buzzwords; they are the very fabric of responsible AI deployment.
How exactly the shift is managed, and what exactly this new financial future looks like, remains to be seen. What is clear is that new regulatory frameworks will be crucial in making the transition a smooth, stable one – and ensuring that AI is used for the good, mitigating risks rather than creating them, and boosting human productivity and innovation, rather than replacing them.
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