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Generative AI has emerged as a transformative force in the banking industry, reshaping the way financial institutions operate and interact with their customers. Unlike more traditional forms of AI, generative AI is easily accessible, quickly driving it to mainstream status.
This article dives into generative AI in banking, exploring its use cases and potential to revolutionize the financial services industry.
The adoption of GAI (generative artificial intelligence) has grown exponentially in recent years. Upon release, ChatGPT reached 1 million users in just 5 days, compared to Twitter’s 2 years.
FintechOS recently surveyed 500 financial services professionals, with 41% of respondents answering that GAI is already being used within their organization today. This rapid adoption is fueled not only by its accessibility, but also due to its versatility.
While traditional AI excels at recognizing patterns and analyzing existing data, generative AI goes one step further. GAI enables individuals and businesses to create a variety of content from scratch, including text, images, audio, video, and even code.
According to KPMG, ‘3 in 4 business leaders believe [generative AI] will be a top three emerging technology over the next 12-18 months.’ That being said, what can GAI do for banks and credit unions?
We’ve broken it up into five categories:
For each of these generative AI use cases, human oversight, data quality, and continuous model tuning are imperative to ensure relevant and accurate results.
The true potential of generative AI in banking emerges when its capabilities are combined. Used together, financial institutions can streamline operations, enhance customer experiences, and create innovative financial products.
Unfortunately, this doesn’t come without risk.
While the future is bright for GAI, utilizing its capabilities still comes with societal, environmental, and legal hazards. Customer data privacy and intellectual property concerns are already being publicly addressed.
Other risks to consider before working with GAI include:
According to the FintechOS survey mentioned earlier, 72% of respondents believe that GAI could eventually replace their jobs. This apprehension may make it difficult to get internal employees onboard, limiting adoption and slowing company-wide growth.
While businesses using GAI are also taking on unknown risks, the potential of GAI is infinite, leaving those who fail to embrace it falling behind.
To navigate the evolving landscape of generative AI successfully, organizations can adopt one of three approaches:
To make the most of generative AI in banking, organizations should consider the following best practices:
Generative AI’s accessibility, versatility, and potential for innovation are already reshaping the way financial institutions operate. While there are challenges and risks to navigate, the future of banking with generative AI holds tremendous promise.
As organizations continue to experiment and integrate these technologies, they will find new ways to enhance customer experiences, streamline operations, and drive financial innovation. Embracing generative AI today ensures that banks are well-prepared for the opportunities and challenges of tomorrow’s financial landscape.
Want to learn more about the future of GAI in banking? Watch this webinar on-demand