The integration of Artificial Intelligence (AI) in the finance sector is not just a technological trend, it is a revolution that is reshaping the landscape of banking, investments, and financial inclusion.
The financial sector’s global spend on AI is projected to rocket over the next three years, growing from $35bn in 2023 to $126.4bn in 2028, representing a compound annual growth rate of 29%. Move too slowly, and a business in this space will be left behind.
Nonetheless, behind the high-profile adopters such as Citi and JPMorgan Chase, AI is not all plain sailing with a regulatory ‘maze’ to navigate and the challenge of legacy systems, among other hurdles.
Transforming the customer experience
AI is playing a key role in transforming the banking industry. One of its most notable applications is improving customer journeys.
Chatbots and virtual assistants, such as those implemented by major banks like Capital One Bank, Bank of America and JPMorgan Chase, allow customers to interact with their financial institutions at any time of day or night, providing instant support and personalised services. These AI-powered systems can answer routine inquiries, provide account balances, assist with bill payments, and even guide customers through complex financial tasks.
Generative AI is pushing the boundaries, for example, global bank ING’s use of advanced chatbot technology. These systems are regularly updated and refined to improve the service offered and provide a cost-effective solution to customer care. Naturally, in a customer-facing service, there are risks to manage, including potential legal hiccups, some of which are explored in a previous emagine blog.
Fraud detection
AI is also being used to enhance fraud detection and improve banking security. Traditional methods of fraud detection often rely on rule-based systems, which can be vulnerable to sophisticated scams. AI, on the other hand, uses machine learning algorithms to detect unusual patterns and anomalies in transactions.
For example, AI systems can identify when a credit card is used in a location far from the cardholder’s usual patterns or when multiple high-value transactions occur within a short time frame. This real-time fraud detection helps reduce losses for banks and protects consumers from financial crime.
AI is also transforming loan underwriting. By using AI to analyse vast amounts of data, financial institutions can assess a borrower’s creditworthiness more accurately and quickly than through traditional methods.
Machine learning algorithms can take into account a wider array of factors, such as spending patterns, income stability, and, where permitted, online activity patterns. This creates a more holistic picture of an individual’s financial health, enabling more personalised loan products.
However, finance being a highly regulated industry means explainable AI is becoming increasingly important. Financial institutions must be able to clearly articulate how their AI reaches decisions, especially for credit or investment recommendations. This ‘glass box’ approach, rather than the traditional ‘black box’ of AI, ensures regulatory compliance while maintaining customer trust.
As in all sectors, AI is intensifying the cybersecurity space, with the arena increasingly becoming one of AI versus AI. As the guardians of wealth, more than any other sector, banks need to remain ahead of the game and on high alert in this AI security arms race.
AI in investments
AI is profoundly impacting the investment world, particularly in areas such as portfolio management, algorithmic trading, and risk assessment.
Robo-advisors, powered by AI algorithms, can provide automated financial advice and have become increasingly popular in recent years. They offer investors an affordable and accessible way to manage their portfolios and provide tailored investment strategies based on an individual’s financial goals, risk tolerance, and desired timescale.
Robo-advisors increase access for people who may not have the resources or expertise to work with traditional financial advisors. Of course, there are risks, among them the possibility of errors, limited customisation, vulnerability to hacking, and the lack of emotional intelligence.
AI is also revolutionising the way investment firms approach trading. Algorithmic trading, which uses AI-powered algorithms to make high-speed, data-driven decisions, has become an essential tool for institutional investors.
These algorithms can process vast amounts of data, such as stock prices, economic indicators, and news events, much faster than human traders can. This means firms can identify profitable opportunities and carry out trades at optimal prices.
High-frequency trading (HFT) is an example of how AI is being used to make thousands of trades per second, with algorithms learning from each transaction and adjusting their strategies accordingly.
Risk management
AI is enhancing risk management practices in the investment industry and banking.
By analysing historical data and identifying correlations between various market factors, AI can forecast potential investment risks and recommend strategies to mitigate them. Machine learning models can also track market trends, assess asset volatility, and even predict future price movements, helping investors make informed decisions and protect their portfolios from market downturns.
In banking, generative AI could fundamentally change financial institutions’ approach to risk management, from efficiencies through automation to regulatory compliance.
Bridging the finance gap
In many parts of the world, especially in developing countries, a large segment of the population lacks access to traditional financial services such as bank accounts, loans, and insurance. AI has the potential to bridge this gap.
Mobile banking platforms powered by AI are particularly important in emerging markets, with mobile phones becoming the primary tool for accessing financial services without the need for a physical bank branch.
AI-driven mobile payment platforms, like M-Pesa in Kenya and Ally Financial, allow users to send and receive money, pay bills, and access credit, all through their smartphones. These platforms leverage AI to assess creditworthiness, process payments, and detect fraud in real-time, creating a secure and efficient financial ecosystem.
Additionally, AI-powered credit scoring models are helping to bring financial services to individuals who have traditionally been excluded from the financial system due to a lack of formal credit history. This has particularly impacted individuals in low-income or rural areas.
AI can use alternative data sources, such as mobile phone usage patterns, utility bill payments, and, where permitted, online activity patterns, to build more accurate credit profiles. This enables lenders to extend credit to underserved populations, helping them access the financial products they need to grow their businesses or improve their livelihoods.
The future of AI in finance
Looking ahead, we can expect further automation in banking services, more personalised financial products, and greater accessibility to capital.
Much of this innovation will be driven by foundation models, the sophisticated AI systems powering today’s generative AI tools, which are now making their way into financial analysis and forecasting. These powerful models can process and understand vast amounts of structured and unstructured data, potentially offering unprecedented insights for financial decision-making beyond customer service applications.
However, this rapid advancement also brings challenges, including concerns about data privacy, cybersecurity, and job displacement in traditional financial roles. A significant part of successfully harnessing AI and producing positive results in this sector will lie in handling the relationship between humans and AI and understanding the vital role that people play. Whilst AI can process vast amounts of data in seconds, humans provide the context, creativity and moral compass.
