Artificial Intelligence and the Next Frontier for Financial Institutions

Artificial Intelligence and the Next Frontier for Financial Institutions
July 13, 2023 6 mins

Artificial Intelligence and the Next Frontier for Financial Institutions

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Artificial intelligence could help banks make better use of customer data and workforce capabilities, in addition to reducing financial fraud. However, financial institutions must be mindful of the risks of AI as well.

Key Takeaways
  1. Banks are uniquely positioned to realize value from artificial intelligence (AI) given their vast stores of data.
  2. Balancing the opportunity and the risks will be critical—and financial institutions will need to explore and implement new governance processes and structures.
  3. With the right approach, banks can gain a significant competitive edge, improve security, enhance the customer experience, and more.

Overview

Artificial intelligence (AI) and its many business applications have long been on business leaders’ radars. But recent media attention and the widespread adoption of tools like ChatGPT are accelerating the conversation and creating a new sense of urgency for leaders.

Financial institutions stand to gain from AI, through applications identifying financial fraud, compliance and risk management to gleaning insights from their vast stores of customer data. Augmenting their workforce and building the digital skills and agility to get more from AI is crucial for realizing the potential from this new technology to further advance growth and business objectives. At the same time, leaders of financial institutions have a keen sense of the risks and regulatory forces that will influence AI adoption. As use cases, testing, and regulatory changes unfold, the financial sector must balance opportunity and risk to usher in the next frontier of their business.

In Depth

There are several key areas where financial institutions can make significant strides in AI, but doing so will also require careful consideration of emerging and changing risks and regulation.

Delivering Better Data

Banks have vast stores of client information that can be used to provide a significant competitive advantage. And AI can help them harness its potential.

“Financial institutions hold a great deal of customer data which AI models can use to generate opportunities, whether they’re recommendations for customers or information they need or ways to speed up customer service processes,” says Peter Keuls, partner, talent solutions at Aon. “By definition, that information also often contains personally identifiable information, so it needs to be used very carefully. And banks know that. They can gain great value from that information and accelerate the insights with the right AI models.”

Already at play in the world of financial services are hedge funds, asset managers and quantitative traders who are applying machine learning to very large market data sets to identify patterns and opportunities for investment and act on them more quickly.

Quote icon

Financial institutions hold a great deal of customer data which AI models can use to generate opportunities, whether they’re recommendations for customers or information they need or ways to speed up customer service processes.

Peter Keuls
Partner, Talent Solutions, Aon
Transforming the Talent Landscape

It’s also no surprise that AI will influence the workforce of the future. At financial institutions, it’s likely that everyone will be touched by AI in some way, both in customer interactions and experience as well as in back-office operations and beyond. By developing the right skills taxonomy and training, leaders can augment their existing capabilities in meaningful ways.

“People will need to learn to work with AI-powered digital assistance and embed AI in workflow processes and customer interactions,” says Keuls.

As they work to bridge the human-machine relationship, banks will be able to serve customers differently, identify efficiencies and accelerate processes to realize more value operationally and in the customer experience.

Fighting Fraud

By identifying high-priority areas where AI can impact their business early, financial institutions can get more value from their AI investments and pilots. One such area is financial fraud, which is growing in number of cases and sophistication and topping financial executives’ agendas. Research shows that in 2022, financial services business saw a 79 percent increase in documented fraud compared with the previous year.

“Banks are paying a lot of attention to financial fraud and how can AI be used to help combat it, along with compliance and any kind of risk management,” says Petra Schmidt, global financial institutions commercial leader, Aon.

As they seek to strengthen their defenses against hackers and manage threats from various sources, AI can help scale their efforts. “Banks are trying to use AI to actually authenticate information and getting more sophisticated, including using biometrics and other information,” says Keuls. By developing use cases that help address immediate problems, such as growing financial fraud, banks can eventually scale and identify other areas where AI leads to efficiency and security gains.

The Risky Side of AI

AI holds promise, but also significantly increases the risks for banks. “Cybersecurity risk is significant, along with discrimination risks and bias in the training data and public data ,” says Keuls. “If the training data is biased, the models built on them will end up being biased unless corrected. So there are significant risks of errors and malfunctions, of deceptive practices, privacy risks, and patent infringement risks if you're using copyrighted content to train your models.”

It’s also important to understand the tradeoffs in speeding up analysis and decision making. “Things happen quickly in markets, and if an AI program has flaws and errors in it, the analysis and insights can have errors which lead to unexpected consequences,” Keuls adds.

Because AI can touch multiple areas of the business, the right risk mitigation strategy is crucial, says Schmidt. It may even differ from other approaches or strategies for risk mitigation given how many people and layers of the business are affected. “Many stakeholders need to be involved—the CRO, CFO, head of HR, CISO,” she says. “The cyber implications of moving to more machine learning and using external tools and third parties as they bring in new technology are significant. Processes and governance will need to be reoriented to this new reality.”

A company’s own AI development, third-party AI tools, and mergers and acquisitions and joint ventures will all be affected, and companies will need to be cognizant of and manage the allocation of liability and new AI risk management guardrails.

As financial institutions define their own risk strategies, they must also stay ahead of and respond to the external forces at play, from regulatory change to peer activity. They’ll need to thoughtfully monitor the trends and influential forces that shape regulation, anticipating what’s next and planning their AI strategy over the long term. “It’s not only the SEC, but companies need to watch how other regulatory bodies are evolving their thinking around AI regulations and data privacy,” says Schmidt. A proactive stance can help companies manage their own response to regulatory change and map their strategy.

Quote icon

The cyber implications of moving to more machine learning and using external tools and third parties as they bring in new technology are significant. Processes and governance will need to be reoriented to this new reality.

Petra Schmidt
Global Financial Institutions Commercial Leader, Aon

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