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How Generative AI Can Transform the Finance Industry

August 18, 2023
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Artificial intelligence is already deeply embedded in the finance industry, however when it comes to Generative AI, companies are just beginning to scratch the surface. In this article, we will delve into use cases in which Generative AI has the highest potential to revolutionize the finance industry.

Artificial intelligence is already deeply embedded in the finance industry. 85% of financial services companies already use AI in some form, with plans to integrate AI even further within the next two years. When it comes to Generative AI, however, companies are just beginning to scratch the surface.

The finance industry faces unique challenges in implementing Generative AI, in large part due to the stringent regulatory landscape designed to protect customer data, ensure fair practices, and maintain market integrity. Despite these and other challenges, forward-thinking financial companies recognize the immense potential of Generative AI and are actively seeking ways to harness its transformative power.

In this article, we will delve into use cases in which Generative AI has the highest potential to revolutionize the finance industry by streamlining operations, enhancing customer experiences, and assisting in making data-driven decisions. 

Summarizing financial documents and unstructured data

Finance professionals working in investment banking, research departments, and asset management sales teams face the challenging and time-consuming task of reviewing and analyzing complex documents. This requires not only reading and digesting large amounts of information, but also understanding and drawing actionable conclusions from it. 

A specialized summarization language model like Summarize API extracts key points from one or multiple documents, while remaining true to their original source. By incorporating this model into a solution, firms can condense large amounts of information accurately and efficiently, reducing the need for financial analysts, researchers and sales teams to manually read and analyze unstructured data. It not only saves time, but also reduces human error, where the eye can misread content or miss key points when faced with an overwhelming amount of information.

Bloomberg has already started using a summarization solution to help both financial professionals and clients address the overwhelming amount of financial news, saving them a vast amount of time and allowing them to stay updated with the latest developments.

Extracting the right information

Financial analysts, asset managers, and research departments within investment and retail banks are required to extract specific information from a large corpus of financial data to promptly address customer inquiries. Consequently, they find themselves diligently sifting through vast amounts of information across multiple documents, in search of the exact answers or solutions.

Here, a multi-doc Contextual Answers solution can make a huge difference. Contextual Answers uses a combination of NLP and machine learning to analyze the context of documents and provide rapid, accurate answers to specific queries.

For example, if you want to find out if a particular action will contravene a rule included in a set of regulations, you can type in your question directly and the model will retrieve the answer from within the text, giving your answer higher levels of accuracy, and avoiding hallucinations. 

One firm using a similar solution is Morgan Stanley. Over the years, Morgan Stanley conducted extensive research on companies, sectors, and markets, which they compiled into a large library. They recently announced a Generative AI-powered question answering solution to enable brokers to ask the library questions and receive answers in an easily digestible format.

Customer Support

Today's customers expect immediate and helpful responses to their inquiries. Financial companies struggle to meet these demands, as they are time-consuming and resource-intensive.

Generative AI emerges as a valuable tool in addressing these challenges by incorporating chatbots capable of addressing customer inquiries effectively. Generative AI-driven chatbots, designed to respond with information solely based on the content contained in a company's database, ensure the delivery of reliable and rapid responses. Through the strategic deployment of Generative AI, financial institutions can strike a balance between operational efficiency and customer satisfaction.

Capital One has already started to experiment with using Generative AI in order to automate and improve their customer service, using AI chatbots that better understand customer queries and concerns. 

Extracting relevant data from transcripts and other documents

Another important but time-consuming task that financial institutions face is extracting relevant information and conducting intent analysis from call transcripts. For example, if a customer calls a bank to make a financial trade, the bank is required, for compliance and regulatory reasons, to document the details of this call in their records in a specific format. 

Here too, Generative AI serves as an effective tool by applying named entity recognition, which can extract specific words from unstructured data and categorize them. Continuing from the previous example, Gen AI can be used to extract details from a customer call, including the quantity, price, time stamp and confirmation of execution. The details would then be formatted to conform to the bank's internal compliance system.

Applying a similar idea, Goldman Sachs is running a POC to extract and classify data from the millions of legal documents they receive, and reformat them to make it understandable to their systems. 

Generation of legal documents for  investment banks 

Generative AI can also streamline and speed up the creation of proprietary legal documents for investment banks. With the implementation of advanced LLMs, Gen AI can assist in drafting complex legal documents with precision and efficiency, tailored to the specific needs and requirements of each investment bank. AI-powered document generation significantly reduces the legal teams' workload, allowing them to focus on higher-value tasks and strategic decision-making. 


Generative AI is already changing the finance industry and leading companies are already finding innovative ways to use it. JP Morgan, Bloomberg, Morgan Stanley and more are already implementing Gen AI to conduct sophisticated financial research, communicate efficiently with clients, and provide better customer support. 

Although Generative AI is still in its infancy, most financial leaders are already recognizing the necessity of examining their current processes and strategizing about where AI could be integrated. Otherwise, they risk falling behind. 

Due to the high stakes involved, integrating Gen AI in the finance industry requires careful attention and collaboration with trusted software partners, as well as constant human oversight and monitoring. When implemented with care, Generative AI has the potential to skyrocket productivity, revolutionize financial processes and transform the entire industry landscape.

Discover more

What is a MRKL system?

In August 2021 we released Jurassic-1, a 178B-parameter autoregressive language model. We’re thankful for the reception it got – over 10,000 developers signed up, and hundreds of commercial applications are in various stages of development. Mega models such as Jurassic-1, GPT-3 and others are indeed amazing, and open up exciting opportunities. But these models are also inherently limited. They can’t access your company database, don’t have access to current information (for example, latest COVID numbers or dollar-euro exchange rate), can’t reason (for example, their arithmetic capabilities don’t come close to that of an HP calculator from the 1970s), and are prohibitively expensive to update.
A MRKL system such as Jurassic-X enjoys all the advantages of mega language models, with none of these disadvantages. Here’s how it works.

Compositive multi-expert problem: the list of “Green energy companies” is routed to Wiki API, “last month” dates are extracted from the calendar and “share prices” from the database. The “largest increase“ is computed by the calculator and finally, the answer is formatted by the language model.

There are of course many details and challenges in making all this work - training the discrete experts, smoothing the interface between them and the neural network, routing among the different modules, and more. To get a deeper sense for MRKL systems, how they fit in the technology landscape, and some of the technical challenges in implementing them, see our MRKL paper. For a deeper technical look at how to handle one of the implementation challenges, namely avoiding model explosion, see our paper on leveraging frozen mega LMs.

A further look at the advantages of Jurassic-X

Even without diving into technical details, it’s easy to get a sense for the advantages of Jurassic-X. Here are some of the capabilities it offers, and how these can be used for practical applications.

Reading and updating your database in free language

Language models are closed boxes which you can use, but not change. However, in many practical cases you would want to use the power of a language model to analyze information you possess - the supplies in your store, your company’s payroll, the grades in your school and more. Jurassic-X can connect to your databases so that you can ‘talk’ to your data to explore what you need-  “Find the cheapest Shampoo that has a rosy smell”, “Which computing stock increased the most in the last week?” and more. Furthermore, our system also enables joining several databases, and has the ability to update your database using free language (see figure below).

Jurassic-X enables you to plug in YOUR company's database (inventories, salary sheets, etc.) and extract information using free language

AI-assisted text generation on current affairs

Language models can generate text, yet can not be used to create text on current affairs, because their vast knowledge (historic dates, world leaders and more) represents the world as it was when they were trained. This is clearly (and somewhat embarrassingly) demonstrated when three of the world’s leading language models (including our own Jurassic-1) still claim Donald Trump is the US president more than a year after Joe Biden was sworn into office.
Jurassic-X solves this problem by simply plugging into resources such as Wikidata, providing it with continuous access to up-to-date knowledge. This opens up a new avenue for AI-assisted text generation on current affairs.

Who is the president of the United States?

Donald Trump
Donald Trump
Donald Trump
Joe Biden
Joe Biden is the
46th and current
Jurassic-X can assist in text generation on up-to-date events by combining a powerful language model with access to Wikidata

Performing math operations

A 6 year old child learns math from rules, not only by memorizing examples. In contrast, language models are designed to learn from examples, and consequently are able to solve very basic math like 1-, 2-, and possibly 3- digit addition, but struggle with anything more complex. With increased training time, better data and larger models, the performance will improve, but will not reach the robustness of an HP calculator from the 1970s. Jurassic-X takes a different approach and calls upon a calculator whenever a math problem is identified by the router. The problem can be phrased in natural language and is converted by the language model to the format required by the calculator (numbers and math operations). The computation is performed and the answer is converted back into free language.
Importantly (see example below) the process is made transparent to the user by revealing the computation performed, thus increasing the trust in the system. In contrast, language models provide answers which might seem reasonable, but are wrong, making them impractical to use.

The company had 655400 shares which they divided equally among 94 employees. How many did each employee get?

94 employees.
Each employee got 7000 stocks
(No answer provided)
X= 655400/94
Jurassic-X can answer non-trivial math operations which are phrased in natural language, made possible by the combination of a language model and a calculator


Solving simple questions might require multiple steps, for example - “Do more people live in Tel Aviv or in Berlin?” requires answering: i. What is the population of Tel-Aviv? ii. What is the population of Berlin? iii. Which is larger? This is a highly non-trivial process for a language model, and language models fail to answer this question (see example). Moreover, the user can’t know the process leading to the answers, hence is unable to trust them. Jurassic-X can decompose such problems into the basic questions, route each to the relevant expert, and put together an answer in free language. Importantly, Jurassic-X not only provides the correct answer but also displays the steps taken to reach it, increasing the trust in the system.

Do more people live in Tel Aviv or in Berlin?

There are more people living in Tel Aviv than in Berlin.
Berlin and Tel Aviv are roughly the same size
(First hit is a comparison between Tel Aviv and Berlin)
More people live in Berlin than in Tel-Aviv

[‘Return population of Tel Aviv’; Return population of Berlin’; Return which is bigger between #1 and #2’]
Step 1: Population of Tel Aviv. Result - 451523.
Step 1: Population of Berlin. Result - 3664088.
Step 3: Which is bigger,  #1 or #2. Result - Berlin.

Jurassic-X breaks down compositional questions, answers the basic sub-questions, and puts together the answer. Importantly, this process is transparent to the user greatly increasing the trust in the system

Dynamic information (like weather and currency exchange rates)

Certain types of information change continuously - weather, currency exchange rates, share values and more. Such information will never be captured by language models, yet can easily be handled by Jurassic-X by integrating it with a reliable source of information. We performed a proof-of-concept on two such features - weather and currency exchange rates, and the design enables quick integration with more sources to solve your use-case.
Weather - a loosely phrased question about the weather elicits an answer from all language models, where language models always return the same answer, regardless of when the question was asked (funny, right?), while Jurassic-X provides an answer based on the actual weather prediction.

I’m going to be in New-York in 3 days. Should I pack my umbrella?

Yes, you should pack your umbrella.
Yes, you should. The weather forecast is rain.
(Links to weather websites)
Yes, you should pack your umbrella, because in New York in 3 days there will be broken clouds and the temperature will be -2 degrees.

Currency exchange rates change much faster than weather predictions, yet the Jurassic-X concept - a language model connected to a reliable source of information - easily solves this problem as well.

How much Moroccan money will I get for 100 bucks?

125 dirhams
100 moroccan dirhams is about 27$.
How much is 100 dollars in moroccan money?
100 dirhams = 10.75 dollars
100 USD = 934.003 MAD
Jurassic-X combines a language model with access to APIs with continuously changing information. This is demonstrated for weather forecasts and currency exchange rates, and can easily be extended to other information sources

Transparency and trust

Transparency is a critical element that is lacking in language models, preventing a much wider adoption of these models. This lack of transparency is demonstrated by the answers to the question - “Was Clinton ever elected as president of the United States?”. The answer, of course, depends on which Clinton you have in mind, which is only made clear by Jurassic-X that has a component for disambiguation. More examples of Jurassic-X’s transparency were demonstrated above - displaying the math operation performed to the user, and the answer to the simple sub-questions in the multi-step setting.

Was Clinton ever elected president of the United States?

No, Clinton was never elected as president of the United States.
Clinton was elected president in the 1992 presidential elections…
Bill Clinton was elected president.
Jurassic-X is designed to be more transparent by displaying which expert answered which part of the question, and by presenting the intermediate steps taken and not just the black-box response

Your Turn

That's it, you get the picture. The use cases above give you a sense for some things you could do with Jurassic-X, but now it's your turn. A MRKL system such as Jurassic-X is as flexible as your imagination. What do you want to accomplish? Contact us for early access

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