Why Some US Banks Prefer Google’s Bard to Boost Productivity and Knowledge-Sharing

In this post:

  • SouthState Bank embraces Google’s Bard, a competitor to Microsoft-backed ChatGPT, to boost productivity and knowledge sharing in banking operations.
  • Other banks are also experimenting with ChatGPT-style technology, exploring its potential in mortgage processes and human resources.
  • Generative AI technology, including large language models, is gaining interest among banks, offering opportunities for improved efficiency and customer interactions.

SouthState Bank, located in Winter Haven, Florida, has enthusiastically adopted Google’s Bard, a competitor to OpenAI’s ChatGPT, and is reaping the benefits of this innovative technology. Unlike other major banks that have restricted the use of large language models due to concerns about sensitive information leakage, SouthState Bank sees Bard as a game-changer that lives up to the hype.

Chris Nichols, the director of capital markets at SouthState Bank, highlighted the remarkable capabilities of Bard at the American Banker’s Digital Banking Conference. The technology excels in text assimilation, knowledge linkage, and summarization. The bank utilizes Bard to summarize policies, and regulatory documents, compose emails, and support marketing copy creation.

Bard vs ChatGPT – What’s the Difference?

The name Bard has geeky roots, which fits with Google’s naming conventions. Bards are a type of playable character in Dungeons and Dragons. Or it could mean a traveling poet who entertains royalty with stories from real life, with the accompaniment of a musical instrument. Google’s Bard is a generative AI. This is the generic name for AI models like ChatGPT and DALL-E that can create new content. Generative AIs can make video, audio, and imagery, but as an AI chatbot, Bard is focused on creating text that answers your questions in a natural and conversational way.

Google wants Bard to supplement the Knowledge Graph Cards you see in Search when making queries that have simple answers. While a Knowledge Graph Card can supply you with a word’s definition or an overview of a person or place, Bard’s responses are meant to address NORA questions (searches with No One Right Answer), as Google calls them.

To do this, Bard first uses LaMDA language models to understand your question and its context. Since LaMDA uses datasets that contain dialogue, it understands nuance and colloquialisms that search engines struggle with. After that, Bard draws on information it finds across the web to form an answer, which is then made into the type of conversational reply you might expect from a real person (again, thanks to LaMDA). Bard’s goal, and all AI chatbots for that matter, is to provide high-quality responses.

Experimenting with Bard vs ChatGPT technology

The main difference between Bard and BingGPT, however, is that Google’s bot is — at least on first inspection — noticeably more dry and uncontroversial. That’s probably by design. Like most AI chatbots, Bard can code, answer math problems, and help with your writing needs. Observers note that Bard is less useful for getting reliably accurate answers to questions, as it often “hallucinates” made-up responses when it doesn’t know the right answer.

SouthState Bank is not alone in exploring the potential of ChatGPT-like technology within the banking sector. Westpac, for example, is testing a large language model from Kasisto to assist borrowers and loan officers in the mortgage process. Generative AI also shows promise in human resources departments, where it can aid in resume vetting and identifying red flags for recruiters.

Michael Haney, head of digital core at Galileo Financial Technologies, stated that most banks are interested in leveraging generative AI technology. Around 80% of Galileo’s bank customers are actively exploring the possibilities of implementing generative AI into their operations. Besides OpenAI and Microsoft, other major players such as Google, Amazon, Oracle, and Accenture, as well as various startups, are also venturing into the field of generative AI.

Implementing generative AI in banking operations

At SouthState Bank, Bard is deployed as an enterprise solution trained solely on bank documents and data. No customer data is utilized, and access to the system is restricted to bank employees. The technology goes beyond being a mere search engine by connecting knowledge and intelligently linking ideas to form a coherent text. Employees leverage Bard to sift through lengthy text threads, summarize discussions, retrieve missed meeting minutes, and perform various tasks such as composing emails, generating expense reports, and analyzing suspicious activity or fraud.

LaMDA, or the less-catchy Language Model for Dialogue Applications, will power the new chatbot. Google has had LaMDA in development for years at this point, formally announcing the project back in 2021.  The training data is one limiting factor and Bard might suffer from similar issues under the hood, but it makes up for this with its Google Search integration, which gives it data on current events in addition to its base LLM training. Unlike Bing Chat, Bard does not look up search results—all the information it returns is generated by the model itself. Users can brainstorm and answer queries with Bard. 

Productivity boost and enhanced learning

Since introducing Bard, SouthState Bank has witnessed a substantial boost in productivity. Around 2,000 out of 5,000 employees currently utilize the system, saving considerable time in finding accurate answers and information. For instance, tasks that previously took an average of 12 to 15 minutes now take mere seconds. Bard’s ability to quickly summarize and provide information assists in training new employees, enabling them to become proficient in specific topics or regulations promptly.

While acknowledging the limitations of ChatGPT and Bard, such as their inability to excel in predictive analytics, Chris Nichols emphasized the need for human oversight due to occasional inaccuracies or hallucinations in the system’s responses. The bank trusts Bard more than humans on average but ensures that references, citations, and expert verification are readily available. To mitigate inaccuracies, SouthState Bank emphasizes the importance of asking precise questions, structuring information effectively, and conducting thorough testing.

Nichols highlighted that the cost of implementing and operating Bard is relatively affordable. It requires an initial investment of approximately $50,000 for production and an ongoing cost of around $30,000 per month for testing and usage. Considering the significant productivity gains achieved by the bank’s employees, this expenditure is considered minimal. SouthState Bank has also established a risk governance committee to oversee the utilization of generative AI.

Substantial improvements in productivity and knowledge-sharing

Chris Nichols envisions large language models becoming the primary means of interaction between employees and customers in the banking sector. Natural language questions are expected to transform how banking services are delivered. While acknowledging the need for cautious and secure utilization, Nichols believes that embracing this technology can lead to substantial improvements in productivity and knowledge sharing within the industry.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decision.

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