This year has brought with it new trends in AI as we continue to move towards a more efficient, sustainable and technology-driven world. One of the industries benefiting from these advancements is financial technology. FinTech is adopting AI more than ever before, thanks to the abundance of available data and increased affordability of computing power. AI presents a range of benefits for the financial sector, of which 80% of banks are highly aware. These include improving productivity, enhancing the quality of services provided, and in effect, increasing profitability. Many innovative FinTech startups showcase AI effectively deployed across all areas of finance, ranging from customer service to cybersecurity and more.
In the first instance, AI is changing the face of online banking customer service, affording consumers more independence and transparency when it comes to managing their personal finances. The natural language processing (NLP) skills and emotional intelligence of chatbots are ever-progressing, proving to be a cost-effective alternative to human consultants. Solutions like Abe.ai ensure consumers enjoy the ultimate personalised experience by analysing their spending habits and providing insights and tailored advice to help them reach financial goals. NLP not only enables seamless consumer interaction, but also empowers the use of AI in cyber security by preventing fraudulent activity, something of great concern given fraud losses are expected to hit $48 billion per year by 2023. AI technologies can harness NLP with text mining and sentiment analysis to accurately identify phishing emails and fraudulent insurance claims, for example. Machine learning models can also flag suspicious transactions by analysing an array of data points such as spending behaviour and location, identifying patterns and irregularities far more quickly and accurately than humans would otherwise be able to.
As AI technologies interpret more and more data, they rely less on human intervention and develop from executing backtesting strategies to making their own informed predictions. This means they are an incredible tool for risk management in the financial sector, outperforming humans in a multitude of ways. Some examples of AI in action include the platform Ocrolus, which digests vast datasets of personal financial records to determine loan eligibility and creditworthiness on individuals with a limited credit history, and market intelligence software like AlphaSense that produces highly accurate market forecasts, empowering organisations in their research and strategic decision making. Robo-advisors use these forecasts to devise personalised asset portfolios for investors, choosing a selection of assets in line with the goals, income and risk preferences of an individual. At an advanced level, AI responds to its own market predictions with imminent action, known as automated algorithmic trading. These systems are far more effective than human brokers because they identify patterns and secure trades in the very first instance before any opportunities are missed. Large amounts of money and effort are therefore invested in algorithmic trading because of the ROI it delivers.
Not only is AI-driven FinTech highly innovative in its own right, but it also has transformative benefits for the financial sector. AI optimises processes across the board, carrying out simple, repetitive tasks efficiently and undertaking those more complex with impressive accuracy and useful insights. It enhances consumer experience, saves time, effort and money and empowers businesses to make data-driven decisions. That said, the positive impact of the “AI revolution” in finance is reliant on us reducing the relevant risks. The 2021 OECD report on AI in Business and Finance outlines extensively the potential problems that come with deploying AI in the financial sector and how these problems can be mitigated to promote the safe development of AI FinTech. One issue is the lack of explainability of AI models, especially those that conduct credit assessments. For example, an individual could miss out on a life-changing business startup grant after their application was deemed unsuccessful by AI software and the bank in question might not be able to explain this. Lack of explainability also hides any biases created by inappropriate or poor quality data. These risks can be averted by regular testing of models – scrutinising both the data inputted and the results produced against baseline data sets. Informing consumers about the use of AI techniques in the delivery of a product is also important to ensure complete transparency and trust.
It is also important to take into consideration the financial and environmental complications of powering these technologies. The growing interest in AI-driven FinTech coincides with a dramatic rise in the cost of power, making it wholly unsustainable to support new AI with fossil fuels – an expensive, unreliable and polluting energy source – and providing further incentive to decarbonize digital infrastructure. Verne Global’s Icelandic data centre runs exclusively on renewable energy, providing reliable, affordable computing power that has zero negative impact on the planet. The move towards Net Zero is as much about ensuring that AI is being developed responsibly as it is about fostering creativity and innovation in the field. Indeed, the bright future of FinTech rests on our commitment to clean, green energy, and our collaboration in realising AI solutions that benefit all.