How AI is transforming financial services: Key roles and functions

A recent study found that the financial sector is the industry using artificial intelligence (AI) the most regularly. The Sapio Research Finance Pulse 2024 report explored consumer and business attitudes in the financial sector across Europe. It found that AI was used in 63 percent of finance roles, followed by IT (44 percent) and accounts (33 percent).

The increased adoption of AI in banking is not limited to Europe, however. Its ability to detect fraud, create hyper-personalized experiences for customers and automate tasks like transaction processing are making it an essential tool for financial institutions around the world.

Let’s take a look at the roles and functions where AI is most typically being used in banking and finance.

Fraud detection

One area that accounts for the high usage of AI in financial services is fraud detection. “AI equips banks with a powerful arsenal to detect and prevent fraudulent activities in real time,” explains Manuel Barragan, a leading consultant in AI, machine learning and IT. “By analyzing vast amounts of data with lightning speed, AI identifies patterns and anomalies, enabling swift and accurate interventions.”

AI can continuously monitor transactions in real-time, allowing for the immediate detection and prevention of fraudulent activities. It can also analyze user behavior over time to spot sudden changes in spending habits, triggering alerts that could point to fraudulent activity.

Financial analysts

A large part of the role of a financial analyst is forecasting future revenues, expenses and growth rates for companies and industries based on historical data and market trends. They then use this information to provide advice to businesses and individuals for them to make informed investment decisions. AI tools can therefore help them to analyze larger datasets than ever before to identify trends and predict financial performance.

The collection, processing and analysis of data can be automated using AI, and intelligent automation can support data entry and report generation, giving analysts more time to focus on strategic thinking.

“Technologies such as machine learning, natural language processing (NLP) and robotic process automation (RPA) are transforming these processes by automating tasks that were previously manual,” says Sandeep Makwana, chief operating officer (COO) at tech consultancy Xebia. “This enables teams to reallocate their time towards identifying new solutions, monitoring potential risks and ensuring regulatory compliance.”

Investment managers

Investment management firms are beginning to use AI-powered robo-advisors to provide automated, algorithm-driven financial planning services with minimal human intervention. These can offer investment advice and portfolio management to customers.

Investment managers and advisors can also use AI-enabled dashboards to make information accessible to them on-demand when interacting with customers.

BlackRock, the world’s largest asset manager, has set up its BlackRock Lab for Artificial Intelligence, through which it is leveraging NLP to find potentially valuable investment insights. Using AI and machine learning in data quality control, BlackRock is also reducing errors in the millions of risk and exposure reports it generates daily, protecting clients and limiting operational risk.

Credit analysts

Credit scoring is perhaps the area that has attracted the most debate in terms of whether AI is set to replace the role of credit analysts. Algorithms can quickly analyze data from multiple sources to predict someone’s likelihood of repaying a loan, however this raises ethical questions. As an example, a recent PwC study cited reports of mortgage algorithms charging Black and Latino borrowers higher interest rates.

On the plus side, automating the credit approval process can speed up what has traditionally been a manual, labor-intensive process, improving service for customers and enabling firms to reduce waiting times.

Chief financial officers

While AI analyzes the data and provides the insights for strategic decision-making, chief financial officers (CFOs) use this information to identify profitability drivers, evaluate investment opportunities and devise strategies, translating these findings in a way that is clear and compelling for other parts of the business. Generative AI tools enable CFOs to prepare reports and draft internal documents, helping to save time.

By integrating AI into their operations, CFOs can improve financial accuracy, enhance strategic planning, reduce costs and drive overall business performance. This can enable finance leaders to move beyond traditional financial roles and become strategic partners within their organizations.

Accountants and tax professionals

According to research by Thomson Reuters, 8 percent of tax firms are currently using generative AI technology, with 13 percent planning to use the tech in the near future. While these numbers are still low, this could significantly increase given that around 30 percent are still considering whether to start using the tech.

The ‘big four’ accounting firms, Deloitte, Ernst & Young (EY), PwC and KPMG, are among those that have invested heavily in AI, using it for document reviewing, analyzing data and building customized client solutions.

At EY, AI is embedded into auditing processes, extracting and analyzing data from contracts and other data sets to identify risks related to fraud.

Additionally, smaller firms specializing in taxes are using AI to automate tax return preparation and help clients predict and plan for future tax implications based on their financial decisions.

Processing payments

J.P. Morgan has been using AI-powered large language models (LLMs) for payment validation screening for several years. This speeds up processing by reducing false positives and enabling better queue management. The result has been lower levels of fraud and a better customer experience, with account validation rejection rates cut by 15-20 percent.

According to the firm, the faster speed of payment processing is vital to improve the customer experience, as most real-time systems still take several seconds to process – too long for customers to walk out of a brick-and-mortar shop with items that automatically debit payment.

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