Banking on AI to Defend the Financial Services Sector

As cyberthreats evolve, financial institutions are leveraging AI to bolster their defenses, streamline operations and protect their assets. Like other data-rich industries, banking, capital markets, insurance and payments firms are lucrative targets with high-value information. Conversely, threat actors - from cybercriminals to nation-states - are harnessing AI to craft more sophisticated attacks, automate their operations, and evade traditional security measures. It is a modern day arms race, and the financial sector is a major target.

Personal identity and financial information, which is often used to commit fraud, remains the most common type of information stolen from financial institutions during a data breach

-U.S. Department of Treasury, 2024

The interconnected systems and complex infrastructure in this sector can make it challenging to secure potential vulnerabilities, making financial services firms an appealing target to unleash sophisticated cyberthreats. According to Juniper Research, online payment fraud is expected to cumulatively surpass $362 billion by 2028, with losses of $91 billion in 2028, highlighting the potential for high financial gains.

To discuss these and other security issues faced in this market, David Moulton, director of content marketing for Cortex and Unit 42, chatted with a few Palo Alto Networks experts. Jason Meurer, principal solution architect, Paul Leonhirth, global advisor, financial services industry (FSI), and Tony Earley, district sales manager had a lively roundtable, which included some predictions.

Enhancing Cybersecurity with AI

As with other industries, like OT/ICS or healthcare, financial institutions are increasingly integrating AI into their cybersecurity strategies. Meurer, who focuses on cloud and AI, notes, "We're seeing enormous changes when it comes to the adversaries using AI to better create things, like phishing schemes and ransomware messaging, and then how we as the defenders are using AI to be better at detecting those threats quicker."

This observation aligns with the U.S. Department of Treasury report:

"AI-driven tools are replacing or augmenting the legacy, signature-based threat detection cybersecurity approach of many financial institutions. AI tools can help detect malicious activity that manifests without a specific, known signature. This capability has become critical in the face of more sophisticated, dynamic cyberthreats that may leverage legitimate system administration tools, for example, to avoid triggering signature detection."

This shift toward AI-powered detection is crucial in an environment where traditional methods may fall short. Meurer further elaborates on the potential of AI in cybersecurity:

"We're going to see advancements in detection and response techniques as we continue to mature. The industry is already fighting fire with fire; that will accelerate. A newer area of concern we are considering is the trained data. You have to be able to test for data validity, free and bias, and make sure that you're getting responses that are not only precise but are also specific to the industry."

Combating Fraud with Advanced AI

Beyond cybersecurity, AI is proving invaluable in fraud detection and prevention. Leonhirth emphasizes the importance of AI in this area: "Everything we do now has to be highly curated for FSI customers, because otherwise they think, 'Why does this apply to me?'"

This tailored approach to fraud detection aligns with the FS-ISAC's findings, stating, "Financial Institutions that have adopted AI and machine learning (ML) models for fraud detection have seen transformative results."

The U.S. Department of Treasury report further underscores this point, saying, "The accuracy of ML-based systems in identifying and modeling fraudulent behavioral patterns correlates directly with the scale, scope (variety of datasets), and quality of data available to firms." This highlights the critical role data plays in enhancing fraud detection capabilities.

Navigating the Challenges of AI Implementation

While the benefits of AI in financial services are clear, its implementation is not without challenges. Earley points out a key concern: "You have to differentiate between the internal use of AI to protect systems and data versus the benefits from a customer experience perspective." This distinction is crucial for financial institutions as they balance security needs with customer service improvements.

He further elaborates on the customer experience aspect:

"Think about how laborious and painful it can be to pick up the phone and talk to somebody in a call center. So those roles may not just be eliminated, but they can be improved dramatically using AI. Think about making a phone call and not having to be asked to validate all your credentials and to have the context with your relationship with the bank and just to be able to streamline and fast track the problem that you have."

Moulton raises an important point about the potential downsides of AI, "Great UX [user experience] and security are often at odds. What gives you confidence that AI is going to crack that nut?" This skepticism highlights the ongoing challenge of balancing user experience with robust security measures, a concern that financial institutions must address as they integrate AI into their systems.

Regardless of how AI is used in the activities of a financial institution, the institution is responsible for adherence to applicable laws and regulations. Complex interactions among data points within an AI model that are not readily observable or explainable by humans can produce unintended or problematic outcomes, and the source of any unfair outcomes may be masked by the model's complexity.

-U.S. Department of Treasury, 2024

The Future of AI in Financial Services Cybersecurity

As financial institutions continue to adopt AI technologies, the landscape of cybersecurity and fraud prevention is evolving rapidly. Meurer predicts, "You're going to see less impact on the UI and more impact on how the autonomy of the AI systems are operating on their own." This shift toward greater autonomy in AI systems could revolutionize how financial institutions approach cybersecurity.

Meurer further explains this concept: "My point around simplifying UI experience is more so for the banks, cybersecurity operators [and] their SOC teams. They will have fewer interactions with the UI because they'll be trusting AI more gradually to be more autonomous and work on its own."

The U.S. Department of Treasury report supports this forward-looking perspective, stating:

"[F]inancial institutions should expand and strengthen their risk management and cybersecurity practices to account for AI systems' advanced and novel capabilities, consider greater integration of AI solutions into their cybersecurity practices, and enhance collaboration, particularly threat information sharing."

However, as Leonhirth reminds us, "There's so much misuse of the acronym 'AI'. AI versus machine learning (ML) and what it really can do for business. If you took the word AI out of a lot of what you see being discussed, it's not much different than the same cyber risk controls we put around existing systems."

This emphasizes the importance of distinguishing between genuine AI advancements and marketing hype, so financial institutions focus on real, impactful applications of AI in their cybersecurity operations - from prevention to detection to response - it needs to help improve overall operational resilience.

Moulton brings up the collaborative nature of AI development in the financial sector: "Are these leaders in these organizations taking their learnings to a safe conference room or into a chat and sharing… Or are they isolated?"

Leonhirth responded by highlighting the importance of industry-level sharing and collaboration, "Firms do this through the FS-ISAC and various working groups collectively as an industry to share intel [and] sightings across the threat landscape to benefit the community."

So, while AI presents significant opportunities for enhancing cybersecurity risk management and fraud prevention in the financial services sector, it also introduces new issues and risks. As institutions navigate this complex point in time, they must remain vigilant, adaptive and committed to responsible AI implementations. The future of financial services cybersecurity will undoubtedly be shaped by AI, but its success will depend on the industry's ability to harness its power while mitigating the risks and fostering collaboration across the sector.

Ready to Learn More?

Adopt GenAI securely and confidently with Unit 42 AI Security Assessment.

For more information on protecting your organization, read Evolving Security Operations for Financial Services.

Also, visit our page on protecting Financial Services organizations.

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