Transforming the Financial Industry with NLP-Powered Chatbots

Tackling a firehose of information is a familiar problem in the financial services industry. In addition to few-shot examples and prompt optimization, another step that can potentially improve the quality of answers is fine-tuning the GPT model by supplying a dataset of questions and answers. Fine-tuning essentially creates a custom GPT model, stored on OpenAI’s systems, that’s available only to your company. It’s a good approach if the nature of information, prompt syntax, and answer formats are very different and domain-specific compared to the standard text generated by GPT.

NLP in financial services

In this article, we showed how large language models can streamline a variety of tasks in finance and banking. While these models are already very capable, the ability to build custom models and pipelines from them boosts their capabilities even more. Here at, we have years of expertise in customizing and fine-tuning large language models and other machine-learning algorithms for multiple industries. But chatbots backed by the awesome power of large language models like GPT-4 are a different breed altogether. Trained on vast quantities of real-world documents, they achieve unparalleled levels of semantic comprehension, abstraction, and accuracy. NLP-based solutions can automatically bring to financial bodies presentations of companies’ management.

How the Finance Industry Is Using the Predictive Power of NLP

The chatbot’s NLP algorithms must be trained on a diverse range of data to account for these differences and ensure that the chatbot can understand and respond appropriately to users from different backgrounds. Financial chatbots may collect sensitive Natural Language Processing Examples in Action information from users, such as bank account numbers, credit card details, and personal identification numbers. Therefore, it is essential to ensure that the chatbot’s data privacy and security measures are robust and comply with industry standards.

NLP in financial services

The financial industry has long relied on data to forecast economic shifts and potential market moves. However, the use of natural language processing (NLP) to analyze textual data is opening a whole new frontier for central banks, asset managers and more. At its most basic, the technology enables organizations to derive insights from written information such as news articles, advertisements, social media and reports. Companies are using machine learning models to predict movement on the stock markets, to assess whether someone is a good applicant for a loan, to combat money laundering, etc.

The NLP spectrum

Recognizing positive or negative sentiment could help companies better predict if the speaker is speaking the truth or not, in effect helping companies make decisions about whether they should invest in a company or not. There is one field of natural language processing that hasn’t evolved at the same speed as the rest of the field, and that is multilingualism. Most research is focused on English, which somewhat limits the type of data NLP models can efficiently process. The difference that multilingualism would make in the overall precision of models cannot be overstated. Since it’s proven to provide multiple benefits across industries, NLP technology has been gaining momentum in recent years, and many companies consider its implementation or development a priority. Strong emphasis is being put on developing the accuracy of the NLP-based solutions in languages other than English.

NLP in financial services

In the banking industry, NLP is being used to speed up negotiations, reduce boring tasks, analyze risks, interpret financial emotions, and design portfolios in addition to automating audits and accounting. Companies can bring in machine learning products, build out a data science team, or, for large companies, buy the expertise they’re looking for — as when S&P Global purchased Kensho. “A company will release its report in the morning, and it will say, ‘Our earnings per share were a $1.12.’ That’s text,” Shulman said.

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It’s more complicated than analyzing customer experience or collecting data about a product’s use. They understand the client’s speech and generate answers using human language. Specialists of financial institutions can analyze them for better decision-making. Content is still king, and the development of advanced generative techniques is a spark of hope for the enterprises that spend a big part of their budget on content creation and management. GPT-3 (3rd generation Generative Pre-trained Transformer) is a deep learning model created specifically for the purpose of text generation and a new frontier of the NLP revolution.

NLP in financial services

The buzzy service is an artificial intelligence (AI) chatbot developed by OpenAI built on top of OpenAI’s GPT-3 family of large language models and has been fine-tuned using both supervised and reinforcement learning techniques. Despite the hype, the possibilities offered by large language models have many in financial services planning strategically. The company offers a chatbot called Assist, which they claim can help banks and financial institutions give their customers personalized banking services and aid them with product discovery using NLP.

Sentiment analysis in documents and news

Many different industries use NLP to great effect; in this blog post, we’ll focus specifically on natural language processing applications in the financial services sector. In order to do so, they employ the NLP for the purpose of extracting the relevant information from financial articles as well as tweets, social media posts, and stock market opinions on StockTwits. That provides their financial analysts with meaningful insights on the market moods as well as trusted and questioned investments or authorities. Natural language processing helps companies from the insurance and financial services industry find relevant information across unstructured resources. That can be particularly helpful in situations requiring rapid knowledge transfer, like company fusions. It’s also a blessing for the companies that sign long-term contracts with many annexes throughout the years.

With this method, the companies can gather insights from the reviews of the apps and services left on the company’s website, e-mails, and testimonials across different channels. The numeric grade system or other scales tend to be quite limiting, not providing the company with much useful information. Processed this way, the user content can serve for advanced analytics in the field of customer satisfaction. Although NLP is often called a branch of machine learning, in fact, these are two subbranches of AI that complement each other. Without machine learning algorithms, computers wouldn’t have the ability to get better at understanding language with time and practice, as happens with humans.

Prediction of Stock Fluctuations

NLP is being used in the finance industry to significantly reduce mundane tasks, speed up deals, analyze risks, comprehend financial sentiment, and build portfolios while automating audits and accounting. NLP, for example, sifts through social media data and finds conversations that might help them improve their services. Major retail banks like HDFC Bank and ICICI Bank deploy powerful customer engagement tools like chatbots to understand client intention. Also, in the financial services industry, client communication is imperative this sector, and NLP tools provide banks with critical information when they interact with customers. Natural language processing (NLP) allows you to glean valuable information from stuff that is underutilized. You could train NLP models to analyze unstructured data, content, and information to address concerns or trends that could influence financial markets.

  • Some companies, such as Microsoft and Facebook, have already created multilingual models that can accurately translate from one language to another.
  • Many major banks have already launched some form of conversational interface that can assist customers with routine requests, such as making payments or getting details about their accounts.
  • Stripe is using NLP to explore customer information to identify interest areas that influence customers positively.
  • Even more, the subtle aspects like lender’s and borrower’s emotions during a loan process can be incorporated with the help of NLP.
  • Companies now realize NLP’s importance in gaining a significant advantage in the audit process especially after dealing with endless daily transactions and invoice-like papers for decades.
  • KAI is a conversational AI platform developed by Mastercard that uses NLP technology to help financial institutions provide personalized customer service.

Traditional data analysis tools were designed to handle structured data and are often ill-equipped to handle unstructured data. As a result, financial institutions are turning to advanced technologies such as natural language processing (NLP) to help them manage and analyze their data effectively. Underwriters need to efficiently analyze data and go through repetitive tasks without making mistakes to make a high-quality prediction. Different AI techniques such as standard regression models and computer vision data are already used to help underwriters make decisions, but technologies such as NLP are also often used even though they are not often mentioned.

A supervised learning process involves fine-tuning a Language Model (LLM) using instruction prompts.

The adoption of NLP in the finance industry has been driven by the increasing demand for automated and efficient financial services worldwide. The use of NLP technology has become increasingly popular among financial institutions as they strive to provide personalized financial solutions that are cost-effective, efficient, and easily accessible to customers. It is already relatively easy to make certain conclusions about a person based on their social media, even for humans. Gaining an understanding of how others feel about a product or a brand can be very useful. In the future, social media might prove to be the ultimate feedback questionnaire, with people sharing their opinions with companies without even realizing it.