The current AI risk landscape for banks
The integration of artificial intelligence (AI) into the United States banking sector is transforming the financial services industry. While many of the benefits are widely known, most notably: driving operational efficiencies, enhancing customer experiences and introducing innovative products — the risks associated with the adoption and utilization of AI in banking have yet to be fully understood. Exacerbating this is the speed with respect to how quickly AI has become adopted, thus presenting challenges for many technology, enterprise and operational risk managers at banks. Additionally, how your organization is using AI and whether that has been properly stress tested is a common concern amongst bank risk managers. As AI technologies continue to evolve, largely due to the current perception of benefits outweighing the known risks, it will be imperative for risk managers to make up ground in identifying and quantifying future risks associated with AI utilization.
Key risks of using AI
Leaders must find a delicate balance between the urgency to implement AI and the related risks inherent to its rapid adoption. Some of the key risks identified for banks with regard to the utilization of AI are fairly intuitive: Legal and security risks, ethical concerns (e.g., bias) and quality and accuracy risks.
Legal and security risks
- Data privacy and security: AI systems require vast amounts of sensitive customer data. While the risk of data breaches, hacking and misuse of customer information isn’t new for banks, the usage of AI certainly amplifies the exposure. Furthermore, continuing to keep up with data privacy and compliance with stringent regulations will not be an insignificant task.
- Intellectual property (IP): Risks associated with AI usage in banks are multifaceted, as AI technologies can affect both the ownership and protection of intellectual property, as well as the risk of infringement. Given the reliance on AI to streamline operations, automate processes and improve decision making, banks face several potential IP-related challenges. These risks must be carefully managed to avoid legal, financial and reputational damage.
- Regulatory compliance: Banks face uncertainty regarding how AI is treated under existing financial regulations and what new regulations might emerge. There’s a fear of inadvertently breaching regulatory standards if AI systems don’t meet compliance requirements, especially in areas like anti-money laundering (AML) and know-your-customer (KYC). The regulatory environment for AI in banking is still changing. Banks must deal with a complex set of financial rules while making sure AI technologies follow rules about transparency, accountability and ethical use. The absence of clear guidelines from regulatory authorities presents an ongoing challenge
Ethical risks
- Bias and discrimination: AI models can unintentionally introduce bias, especially when trained on historical data that reflects societal inequalities. This could result in discriminatory outcomes, such as biased lending decisions or unequal access to financial services. AI also raises other significant ethical questions, particularly in areas like customer decision making and transparency. Risk managers are concerned with the public’s perception of AI-driven decisions, especially if customers feel that algorithms are making choices about their finances without clear explanations.
Quality and accuracy risks
- Hallucinations: This refers to situations where AI systems produce incorrect or fabricated information that appears plausible but is actually false. In a highly regulated sector like banking, where accuracy and reliability are crucial, this is a particularly acute risk that can manifest itself in the form of poor financial decisions, fraud detection failures, compliance violations, and/or a loss of customer trust.
- Vendor and automation reliance risks: As banks increasingly rely on third-party vendors for AI tools and to automate various processes, they increase their vicarious liability. These liabilities can have far-reaching implications, ranging from service disruptions to regulatory and compliance issues.
How can we mitigate AI risks for banks?
To properly address these risks, bank risk managers must take a balanced approach, combining technology with robust risk management strategies, ethical frameworks, transparency/strong customer communication and regulatory alignment. AI risks are growing and are increasingly interconnected, requiring a dynamic approach. Some key strategies to mitigating AI risks are:
- Build a robust governance framework that contains clear policies for AI usage, including roles and responsibilities and establishes an oversight committee to monitor AI initiatives to ensure alignment with organizational goals.
- Establish effective bias mitigation strategies that mandate regular bias audits of AI models to identify and address potential discrimination and that take care to use diverse datasets and employ techniques to ensure fair outcomes in decision-making processes.
- Enhance customer communications to provide increased transparency and explainability. Banks should strive for transparency in AI decision-making processes by utilizing explainable AI techniques, thus providing customers with clear explanations of how AI affects their financial decisions.
- Increase regulatory and compliance alignment efforts to stay updated on relevant regulations and ensure AI systems comply with legal requirements. Effective risk managers will engage with regulators to understand expectations and collaborate on best practices.
By implementing these strategies, banks can significantly reduce the risks associated with AI while harnessing its potential to enhance efficiency and customer experience.
Continue to manage your risks and innovate in the age of AI
AI is rapidly changing the U.S. banking sector by improving operational efficiencies, enhancing customer experiences and creating new opportunities for financial innovation. While challenges related to data privacy, bias and regulatory compliance remain, the future of AI in banking looks bright. As technology evolves, banks must strike a balance between innovation, ethical considerations and risk management to fully realize the benefits of AI. The next few years will likely witness widespread AI adoption, reshaping the financial landscape for both institutions and consumers alike.
Find out how WTW can help your organization navigate the complexities of AI implementation. Reach out to a WTW colleague or contact us here.
Disclaimer
WTW hopes you found the general information provided in this publication informative and helpful. The information contained herein is not intended to constitute legal or other professional advice and should not be relied upon in lieu of consultation with your own legal advisors. In the event you would like more information regarding your insurance coverage, please do not hesitate to reach out to us. In North America, WTW offers insurance products through licensed entities, including Willis Towers Watson Northeast, Inc. (in the United States) and Willis Canada Inc. (in Canada).
