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Language Model Models (LLMs) are the latest buzzword of the tech industry, and for good reason. The most popular LLM today – OpenAI’s ChatGPT – has proven to be a powerful tool for day-to-day tasks, from summarising huge chunks of text to drafting emails. It’s no surprise that tech-savvy employees have started to use it to boost their efficiency at work. But using third-party general LLMs have risks and limitations.
Recently, Bloomberg, the financial software, data, and media company, has launched its own LLM called BloombergGPT. The model is designed specifically for financial traders and investors, and marks a significant milestone in the use of LLMs in financial services. We’ll briefly discuss BloombergGPT’s capabilities and its implications on the financial industry moving forward.
BloombergGPT is specifically designed to help traders and investors analyze news and data, enabling them to make better investment decisions. BloombergGPT is able to detect whether news headlines indicate a bullish or bearish sentiment, for example. The finance-specific LLM analyzes news stories, earnings reports, social media feeds, and other sources of information to identify relevant trends and patterns.
And similar to Bing Chat, BloombergGPT can answer questions like “Who is the CEO of Citigroup Inc?” – and do it fairly accurately. That’s because BloombergGPT is trained on 40 years of proprietary data that enables the LLM to identify corporations, business entities, and even stock ticker symbols. Bloomberg will be integrating its LLM into one of its core services, the Bloomberg Terminal, enabling subscribers to get even more value out of the search database.
BloombergGPT is a powerful example of how LLMs can be used to power the financial services industry, and its potential is only being discovered.
The example of BloombergGPT shows that LLMs have the potential to transform the financial services industry by automating and streamlining many processes. Finance-specific LLMs can analyze large amounts of data, identify patterns and trends, and make predictions with a high degree of accuracy.
Another way LLMs can be used in financial services is in fraud detection. Trained on historical data and typical transaction patterns, LLMs can be used to identify potential fraud by detecting unusual activity. Rules can be set up for AI assistants to alert financial institutions when this occurs, enabling banks or investment houses to take immediate action like blocking transactions or accounts to prevent further damage.
A more common use case which is already being down with general purpose LLMs are AI bots in customer service. And while general LLMs already improve customer service efficiency, having a finance-specific LLM can improve outcomes even further. An industry-focused LLM can process specific finance-related customer inquiries and provide more fine-tuned responses. With finance-specific LLMs, AI bots may be able to handle more complex queries, further reducing the workload for customer service representatives and improving response times.
There are various other ways to use custom LLMs to boost efficiency and streamline operations in finical services. It can be deployed for employee onboarding, providing essential information for new staff. In a highly regulated industry such as banking and insurance, domain-specific LLMs can be used to analyze regulatory documents and identify relevant sections that require action for compliance.
And while BloombergGPT is impressive, it only functions in one language – English. This limits its application into broader markets and geographies. The future of LLMs in financial services are multi-lingual, domain-specific models that perform well in narrow finance-related tasks and also general queries.
Finance LLMs are not new – in fact, a research paper on financial LLMs was already published six months before BloombergGPT was announced. Still, these domain-specific AI models have the potential to revolutionize the financial services sector. They can help automate and streamline processes, improve accuracy and efficiency, and reduce the risk of human error. Financial institutions that embrace LLM technology will likely gain a competitive advantage over their peers.