Bridging the AI Divide: How Banks Can Responsibly Adopt Large Language Models (LLMs)
By Oliver King-Smith, founder, SmartR AI
The meteoric rise of chatGPT has propelled large language models (LLMs) into the mainstream, showcasing their remarkable ability to generate human-like text and dialogue. While major tech firms are eager to capitalize on this technology, LLMs also pose risks related to data privacy, security, and ethics that should give financial institutions pause.
On the other hand, banks and financial services firms, failing to craft a responsible LLM strategy, gamble falling behind institutions already leveraging these tools to enhance customer service, streamline operations, bolster cybersecurity, and more. The divide between AI leaders and laggards is poised to widen over the next several years.
So, how can banks adopt LLMs safely?
The critical factor is maintaining control over data access. Publicly available LLMs, such as chatGPT, operate as “black boxes,” trained on bloated, unspecified datasets, and their access is subject to the whims of the free host site’s rules and stability. In the event of public LLMs blocking an enterprise, there is a danger of losing access as well as potentially revealing valuable insights into strategic advantages or weaknesses to competitors. More significantly, banks utilizing these models face the potential exposure of confidential customer information and sensitive financial data, thereby risking a violation of data privacy regulations and the erosion of consumer trust.
Private enterprise models mitigate these potential hazards by allowing banks to control LLM training and data access. These bespoke models only interact with a bank’s internal systems and databases. By containerizing LLMs on their secure servers, financial institutions limit outside exposure, enabling them to train models on appropriate datasets that exclude personal customer information when needed.
Specialized banking LLMs (Language Model Machines) excel in the collection, assimilation, and organization of decades’ worth of industry-specific tribal knowledge. This valuable information, often scattered across the enterprise through dispersed emails, documents, manuals, and other channels, sets these LLMs apart from general models like chatGPT. They offer more targeted and relevant insights.
By serving as a cohesive and centralized library, these assets can transform into a query-able resource, essentially becoming the enterprise’s virtual expert on various aspects, including financial risk, regulations, product design, and more. This centralized approach enhances accessibility and efficiency, allowing for streamlined decision-making and a more informed understanding of key areas within the banking domain.
Adopting private LLMs requires deliberate planning around controls and use cases. Banks should conduct risk assessments, define strict access policies, train models ethically, and implement protocols to prevent storing personal data in the LLM. With the right governance, these tools can responsibly automate processes and enhance decision-making.
AI is a new technology, but good engineering practices still apply. Focus on specific use cases and start by building well-curated datasets. By planning upfront and cleaning your data, you can develop powerful LLMs which remove the hallucinations and other artifacts the big general models often suffer from. The better defined the use case, the stronger the model will be. As a rule, three specialized LLMs are better than one big LLM. By keeping focused, project risk, cost, and schedule can be better managed.
Today’s AI bellwether companies rely on careful evaluation and deployment of LLMs tailored to their businesses. As banks follow suit, responsible adoption of private enterprise models helps bridge the AI divide by unlocking innovation while prioritizing trust. The opportunity to strengthen operations, security, and service awaits those financial institutions willing to engage thoughtfully with this ground-breaking technology.
Global Banking & Finance Review
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