Artificial Intelligence (AI) has already changed finance. Robo-advisers,  data-analysis, investment decisions, content creation: every stage of the financial services supply chain is being disrupted. In 2023, financial companies spent $35 billion on AI. This is expected to reach $97 billion by 2027. In truth, financial services have always been a technological arms race. Whether it was early access to Bloomberg Terminal or traders buying real estate in the New York Stock Exchange’s data-center so their cables could be shorter and their exchanges a nano-second faster. This is just the next phase.

Here we take a look at how our new robot overlords are already impacting financial services and what the future might look like.

 

How is AI already being used in finance and fintech?

Financial institutions (FI) of all stripes already rely on AI to automate routine tasks, handle algorithmic trading, analyze market and trading data, detect fraud, and cut costs.

Automation and data analysis  

Two-thirds of financial institutions use artificial intelligence to analyze large amounts of data, from global markets to customer tickets. According to KPMG, by 2027 99% of banks will be using AI to generate automated business reports.

Predictive analytics and portfolio management 

AI is being used to provide investors with data-driven insights and sophisticated analytics for smarter investment decisions. Through predictive analytics, risk assessment, and market sentiment analysis, AI helps investors with asset allocation, diversification, and real-time portfolio monitoring.

Fraud detection and risk management  

AI is being used behind the scenes to reduce risk in banking operations. Banks can crunch large amounts of data, better analyze and detect threats, react to cyberattacks, and even identify customer concerns more rapidly with natural language processing and sentiment analysis. For example, in early 2024, Mastercard launched a generative AI model it claims can boost fraud detection by up to 300%. Its proprietary algorithm is trained on data from the roughly 125 billion transactions that go through the company’s card network annually. With this data, AI can understand relationships between merchants and predict where fraudulent transactions are taking place.

Back-office automation 

Generative AI can also make routine tasks less time-consuming, speeding up the process of researching and drafting quarterly reports, answering customer concerns over the phone, and perhaps, one day, even predicting and alerting banks to major life events like marriage, kids, and home ownership.

 

 

What’s the impact of AI on the finance industry? 

Aside from helping bankers make better decisions, artificial intelligence in finance is fundamentally changing work and profit.

Recent research from EY-Parthenon looking into how decision-makers at retail and commercial banks around the world viewed the opportunities and challenges of generative AI. Respondents to their survey cited three main areas where the technology  is changing ways of working at their banks.

• Enabling greater productivity by automating sales-related activities (66%)
• Enhancing existing technological capabilities (63%)
• Accelerating broader innovation (54%)

We recently spoke to Lex Sokolin, a finance entrepreneur and investor, on our podcast episode, “How AI Will Transform Fintech Innovation” all about the existing and future benefits of AI integration for FIs.

Boosting GDP 

According to Lex, one of the biggest impacts will be on the industry’s GDP.  “McKinsey estimates [a GDP growth] of somewhere between two to four trillion coming from generative AI,” he said, partially because generative models are able to deliver a fundamentally new banking experience. 

Transformed roles for the entire supply chain 

Banking roles are set to radically change in scope. Recently, a Citigroup study projected that almost 70% of finance jobs will fundamentally change with automation, and according to the Financial Times, that’s even more likely for jobs that pay north of $100K. And even if these jobs don’t disappear altogether, they’ll likely be augmented or upgraded to center around more strategic work. Lex agrees and predicts that instead of bankers and finance workers running spreadsheets, analytics, and predictive metrics by themselves, they’ll be partnered and paired with co-pilots or AI agents. “It’ll be an economic transformation,” he told us. 

Harder for financial marketers to cut through 

The financial content landscape is already crowded. AI is only going to increase that as generative AI allows FIs to pump out content at an alarming rate. That means financial marketers need to make sure their content stands out and cuts through. But while AI and large language models (LLMs) will likely enhance the amount of sub-par content, they also present forward thinking creators with a chance to produce personalized and customized content far quicker than before.

 

 

Use cases for generative AI in finance 

Generative AI such as ChatGPT and Google’s Gemini have the potential for any number of use cases, many of which are already being put into action, as discussed above.  

Text generation and processing

Generative AI can be used to craft research reports, draft documents, and summarize lengthy articles. It automates routine writing tasks, allowing financial analysts to focus on more strategic activities. AI tools can swiftly process vast amounts of data, extracting insights and presenting them in clear, concise reports. Even traditional financial news outlets are integrating AI into their content. Take Narrativa, an AI tool developed by the research team at the Wall Street Journal. Expert editors and journalists review the output, but tools like these can help contribute investment data and content to help small teams create better articles for their audience. 

Chatbots and virtual assistants

AI-driven assistants can handle inquiries 24/7, offering quick, accurate responses to client questions. Beyond simple Q&A, they can guide customers through complex financial processes, enhancing the overall user experience. Automated agents can handle calls with human-like speech, providing personalized assistance without the wait times. AI also narrates videos, making content more engaging and accessible.

Image and video creation

AI can generate high-quality images and videos tailored in a fraction of the time it takes an individual. Finance professionals can utilize this for everything from charts to advertisements. 

Dynamic financial planning and automated finance management

AI algorithms analyze financial data in real-time, enabling dynamic planning and decision-making. Automated tools can manage budgets, track expenses, and even optimize investment strategies, empowering financial advisors to deliver superior results.

 

 

Benefits of AI in finance 

Recent research from EY-Parthenon looking into how decision-makers at retail and commercial banks around the world viewed the opportunities and challenges of generative AI. Respondents to their survey cited three main areas where the technology  is changing ways of working at their banks.

• Enabling greater productivity by automating sales-related activities (66%)
• Enhancing existing technological capabilities (63%)
• Accelerating broader innovation (54%)

Elsewhere, benefits include: 

Less time spent on administrative tasks

 AI can carry out tedious tasks like data entry and document processing. By automating these historically labor-intensive activities, finance professionals can focus on more strategic initiatives, boosting productivity and innovation. This can also be applied to banking processes, where AI can automate routine activities such as loan processing and account management. This automation speeds up applications, reduces errors, and ensures compliance with regulatory requirements.

Better customer and company security

As we’ve mentioned, AI excels in safeguarding financial transactions by monitoring customer and purchase behavior. It identifies anomalies and flags suspicious activities in real time.

Improved customer experience

 AI-powered tools personalize customer interactions, providing tailored advice and recommendations. Chatbots and virtual assistants offer instant support, enhancing satisfaction and loyalty by delivering seamless, round-the-clock service.

 

 

Challenges of AI in financial services

With great power, however, comes great responsibility and using AI in finance isn’t without challenges. 

Stakeholder buy-in and support 

Banks, like Big Tech, face regulatory roadblocks, vast volumes of data, and customers' doubts about whether generative AI can be trusted with their private financial data. This means it’s not always easy to secure resources and corporate buy-in for large-scale AI projects, especially those that involve some measure of uncertainty and risk in exchange for a reward. Managers might also lack the organizational support needed to implement AI long-term, not just for the next earnings call or short-term executive update. 

Finding the right workers 

Especially for startups and small-to-mid-size banks, attracting AI-skilled workers can also pose a challenge. “You can’t [always] afford an OpenAI researcher,” Lex explained to us.

 

 

Governance of AI in the finance industry 

Regulators, especially those in the European Union, don’t always favor experimental use cases, especially those that might expose customer data. And if FIs expand globally, AI compliance only grows more difficult – what regulators accept in San Francisco may not fly in London, Singapore, or Shanghai. Already, nonprofits and organizations like the Distributed AI Research Institute and the Aspen Institute have started raising awareness of the potential risks of AI, from bias in mortgage approvals to unfair scoring systems for onboarding tasks. 

Protocols and guidelines for responsible AI 

Overall, governing AI requires a firm grasp of transparency, accountability, ethics, and global compliance. Dabbling in biometrics or facial recognition? That’s considered a high-risk activity, according to the EU’s AI Act. The best route is to develop transparent and public-facing protocols describing your use of AI in finance. This way, you can build customer trust while showing regulators they’re committed to safe artificial intelligence.  Some banks even set clear rules for when and where AI can act as an independent agent, requiring that humans stay in the loop if the decision might have a negative impact. 

LeackStat 2024