4 Steps to Dodge Trouble When Using Generative AI

Before financial institutions can reap the benefits of generative artificial intelligence they must take steps to avoid the potential pitfalls that accompany adoption of this technology. Here are key ways that banks and credit unions can advance intelligently into this technology without stepping on landmines.

In an era marked by rapid advancements in artificial intelligence technology and growing interest in generative AI overall, powerful language models like OpenAI’s GPT family are finding diverse applications across industries. However, as banks and credit unions embrace such technology to streamline processes, enhance customer experience, and optimize operations, they must remain vigilant against potential ethical and data privacy concerns that may arise.

Here are four key issues that demand immediate attention, along with potential solutions that financial institutions can adopt to ensure responsible implementation.

1. Keeping Misinformation Out of the Mix

AI models like the GPT family, while remarkable in their capabilities, are not immune to generating text that might inadvertently promote misinformation. For financial institutions, this poses a substantial risk as inaccurate information could lead to poor decision-making, erroneous financial advice and customer mistrust. Banks and credit unions must take proactive measures to combat misinformation in AI-generated content. (We’ll treat with preventing bias, a related issue, in detail in the final tip.)

To counter this challenge, IT staffs at banks and credit unions should consider creating educational materials and resources to help internal clients understand the potential risks of misinformation and how AI algorithms work. This could be in the form of articles, videos or interactive content explaining the limitations and biases of AI systems.

“When leveraging generative AI, financial institutions must emphasize the importance of human oversight.”

Transparent communication with customers on this topic will go a long way towards fostering trust and confidence in the services offered. Additionally, banks and credit unions should implement user feedback mechanisms for clients to report any misinformation they come across, enabling them to promptly identify and address any potential issues.

For example, there are various user feedback mechanisms that could be implemented in a mobile banking app that uses OpenAI-powered chatbots. One such solution is an in-app feedback form that allows users to rate their overall experience with the chatbot and provide specific comments about its performance. This is also a great way for financial institutions to gauge overall user sentiment and identify areas of improvement.

When leveraging generative AI, financial institutions must emphasize the importance of human oversight. A team of human experts should review and validate the AI-generated output to catch potential misinformation or harmful content. Moreover, by actively collaborating with AI experts, researchers and providers, banking institutions can also ensure that these models are continually refined and trained on accurate and unbiased data.

Read more: Notably Quotable: What Banking and Tech Leaders Think of Generative AI

2. Protecting Privacy and Data Security from Generative AI Risks

Banks and credit unions deal with highly sensitive and personal customer data, such as financial transactions, account information, and social security numbers. Models like OpenAI’s require access to this data to function properly, which raises valid concerns about the privacy and security of customer information.

If customer data is compromised, it could lead to identity theft, fraud or other forms of cybercrime, resulting in severe reputational damage for banks and credit unions and potentially serious financial losses for both the financial institution and their customers.

“Banks and credit unions should strive to obtain explicit consent from customers before using their data for AI-driven interactions.”

By investing in robust encryption and access controls, institutions can ensure that sensitive data remains well-guarded from threats. Implementing regular security audits is also a great way to detect and resolve vulnerabilities before they escalate into a full-blown crisis.

Additionally, instituting comprehensive data protection policies and obtaining explicit consent from customers regarding data usage will reinforce a culture of data privacy and accountability.

For example, banks and credit unions should strive to obtain explicit consent from customers before using their data for AI-driven interactions. One way this can be achieved is by offering clear and easily accessible opt-out options for those who prefer not to engage with AI-powered services. In doing so, financial institutions will demonstrate their commitment to prioritizing customer trust and autonomy.

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3. Lack of Control Over the End Product

The expanding capabilities of AI models raise concerns about who holds control over the generated content. For banks and credit unions, relinquishing control over AI deployments could lead to unintended consequences, such as the dissemination of misinformation or inappropriate content to customers.

To mitigate this risk, financial institutions should establish stringent guidelines for the use of AI models and closely monitor their applications. This will ensure that only the most relevant and responsible content reaches their customers.

This requires creating a robust internal review process and setting boundaries on the type of content that can be generated. In addition, banks and credit unions must maintain control over AI-driven communications. Overall, striking a balance between maximizing AI’s potential and overseeing its output is key to ensuring responsible and ethical use.

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4. Equity and Accessibility Can Support Fair Banking and Avoid Bias

In the pursuit of optimizing services through AI technology, there lies a risk of unintentionally perpetuating societal inequalities. Biases in the training data can result in preferential treatment of certain groups or individuals, leading to unfair practices in financial services.

For example, if an AI model is trained on historical data that reflects discriminatory practices from the past, it may perpetuate these biases in current decisions. Although OpenAI recognizes the importance of addressing these issues and is working towards reducing biases and increasing accessibility to its technology, banks and credit unions must also tackle this challenge head-on.

“Banks and credit unions must prioritize diversity and inclusion in their own AI development teams.”

For starters, begin with the human factor: Banks and credit unions must prioritize diversity and inclusion in their own AI development teams to establish a more holistic approach when identifying and rectifying potential biases. Actively seeking input from individuals of diverse backgrounds is vital to implementing AI systems that are more inclusive and equitable in their operations.

Financial institutions should also consider working closely with AI providers to ensure that algorithms are trained on diverse and representative datasets. Further, investing in ongoing research and development to reduce biases and ensure that AI benefits all customers, regardless of their backgrounds, should be top of mind for banks and credit unions.

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By acknowledging the challenges that come hand-in-hand with implementing this technology, financial institutions can navigate the AI landscape with confidence. This will pave the path towards an AI-augmented future that offers unparalleled benefits to both institutions and their customers while upholding the highest standards of ethics and data privacy.

Ultimately, embracing the responsible use of AI will not only strengthen customer trust but also drive sustainable and inclusive growth in the banking sector.

About the author:
David Donovan is head of financial services, North America, at Publicis Sapient

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