LLM in Finance: Key Use Cases and Benefits

The finance industry is generally seen as the most technical and lethargic industry. However, in the last decade, there has been an exponential growth of digital technologies in financial operations. The finance industry isn’t slow but is highly cautious. If we observe the tech evolution of financial institutions, we will find that they are riding the tech wave but with a life jacket. Popular technologies like online banking, cloud services, and others have occupied core pillar positions in the finance industry.

Currently, the finance industry is discovering all the prospects of artificial intelligence technology. It is identifying what use cases can be leveraged to get maximum results. For e.g., the recent trend of GenAI is gaining traction as banks and NBFCs are using it for customer services in the form of chatbots.

Large Language Models (LLMs) are one such subset of AI that has been gaining high importance in the last few years. LLM in finance is expected to become the next major thing. Large language models (LLMs) are emerging as a game-changing innovation that offers unprecedented capabilities in natural language processing and data analytics. In this article, we will delve into various aspects of LLM use cases in finance, its applications, and much more. Let’s begin with the basics.

What is LLM in Finance?

The Large Language Model (LLM) is a subset of generative artificial intelligence (GenAI) that can understand, process, and generate human-like language. A vast amount of data is used to train the large language model and provide the required knowledge to the model. Thus, it can perform a huge variety of natural language processing tasks.

During its nascent stage, the language models were designed to tackle text-based tasks. However, SaaS companies are now taking LLM models to other levels, like images, videos, and audio.

There are various applications of the LLMs, such as content creation, text summarization, data analysis, and others. Large language models, as opposed to traditional task-driven NLP tools, are far more versatile and capable of recognizing a broader range of data patterns in human language.

In the finance industry, the LLM models can analyze extensive financial data, extract valuable insights, and facilitate informed decision-making. These models can help in various aspects like risk evaluation, fraud detection, customer support, compliance, and investment strategies.

Latest Statistics of LLM in the Financial Industry:

Here are some important statistics of the Large Language Models:

• The global LLM market is set to grow from $1,590 million in 2023 to $259,817 million in 2030. The CAGR during this period will be 79.80%.
• In the North American market, the market will reach a value of $105,545 million by 2030 with a CAGR of 72.17%.
• By 2025, it is estimated that there will be 750 million apps using LLMs.

Such impressive statistics reflect the potential of the LLM and why financial institutions should also leverage LLM AI in finance. That’s the reason GenAI is also growing in various industries generative AI in education is a growing field.

Key Applications of LLM in Finance

There are varied and high-utility use cases of LLM in the financial industry. Let’s know about the popular LLM applications in finance:

a. Better Customer Experience:

You can deliver a much better banking experience to your customers through LLM. With natural language processing technology, LLMs can analyze all the major aspects of a client’s account, including intent, and offer fine-tuned solutions in real-time. This will result in higher customer satisfaction and customer loyalty.

Banks, NBFCs, and other financial institutions can improve customer experience by creating AI-based chatbots that provide 24 x 7 personalized support, text generation, financial advice, and other services. It will exonerate employees from routine manual tasks and allow them to focus on more productive tasks.

b. Back Office Automation:

In the financial industry, there are various documents from which the information needs to be fetched and made sense of it. For example, employees have to do the same in contracts, policies, credit memos, trading information, and other areas. The virtual assistants based on LLM can help in this task. Plus it will also help extract data from documents like loan applications and financial statements. There will be no human-prone error, and the processing will get faster.

c. Financial Analysis and Risk Management:

LLMs enable banks to foresee trends and assess market risks by analyzing large amounts of unstructured data such as news stories, reports, internal papers, and regulatory filings. This enhances risk management, decreases exposure to market volatility, and strengthens regulatory compliance, leading to higher returns and improved financial performance.

d. Identifying Anomalies and Fraud:

The LLM models can greatly help financial institutions to identify fraud by analyzing vast datasets and detect if there are any unusual data patterns that indicate suspicious activity. These models can also track transactions in real time so that banks can quickly investigate anomalies, such as signal scams or account manipulation. These models reduce false positives while also protecting client accounts and assets by recognizing existing fraud tendencies and reacting to evolving threats.

e. Faster Processing of Loans and Assessment of Creditworthiness:

Assessment of clients’ creditworthiness has always been a manual process that takes a lot of time. With LLMs, this process can be automated. The LLMs can analyze a wide range of complex information, including money transaction history, spending patterns, and other essential data points. This makes it possible to conduct a more thorough assessment that provides lenders with a more accurate picture of risk while guaranteeing that borrowers with short credit histories are treated more equitably.

f.  Investment Research and Analysis:

The machine learning solutions are capable of summarizing and analyzing financial reports, earnings calls, and market data at a speed and scale impossible for human analysts. The LLMs can also be used to generate comprehensive investment research reports that synthesize information from various sources, providing deeper insights.

Benefits of Using LLM in Finance

Utilizing domain-specific LLMs to empower finance infrastructure offers a plethora of benefits, including:

a. Deliver Meaningful Insights by Processing Financial Data:

LLMs are generally trained using real-world financial data from large-scale sources like Bloomberg, US banks, and Capital IQ. These LLMs can thus help investors gain insights into the shares they are thinking of buying. Once the LLM model learns how to identify patterns and forecast rises or falls in share prices, it can make these calculations even on the go without making any changes in the code.

b. Gives Competitive Advantage:

There is cut-throat competition between financial institutions, whether they are insurance companies, banks, or NBFCs. Financial institutions are in unrelenting competition with their peers and are looking at every possible prospect to simplify services for customers.

Technology can play a key role here as it can give a significant edge by automating workflows, increasing speed, optimizing current service offerings, and using new avenues for better service offerings. LLM will directly help the institutes in this regard.

c. Accurate Data Analysis:

The LLMs are capable of performing complex financial calculations and analyzing unstructured data. Thus, they are highly useful for those financial institutions who are looking to improve their decision-making processes. The LLMs can be used to analyze massive data volumes like financial reports and quickly generate summaries and insights that aid in risk assessment.

Analysts can swiftly and effectively make well-informed judgments thanks to the value of this generated language, and LLM performance is enhanced by large data analysis without the need for human participation. Therefore, the introduction of LLMs has made it possible to estimate stock prices and income in a much better way.

d. Modernizing Infrastructure:

AI development services always help bring a modern touch to the existing infrastructure of financial institutions. AI brings fast, scalable, and agile computing resources essential for competitive banking success. It is not necessary to always invest in hardware to achieve this success.

The banking application development with LLM technology can uplevel customer services and improve the speed of backend operations. To develop, test, implement, and track models consistently and reliably, banks will always require sophisticated, scalable AI models, strong toolkits, and standardized processes.

e. Meeting Regulatory Requirements:

There are financial regulators that provide certain guidelines, compliances, and regulations that banks always have to follow. Thus, banks are under consistent scrutiny for compliance. They have to create and submit timely reports with no errors and other financial analyses.

The intricate compliance procedure necessitates gathering verified data from various systems and sources. By automating data collecting and enhancing decision speed and accuracy, LLMs help with this high-stakes task. By doing this, a company can fulfill its compliance requirements, avoid penalties, avoid legal action, and maintain the integrity of its brand.

Popular LLM Models Used in Finance

The financial LLMs are the models that are specially curated for the financial technologies and processes. Currently, there are limited LLM models for finance, and they are also expensive. It is not expected that financial organizations will open their platforms due to internal regulations. Here are a few popular LLM models used in finance:

1. BloombergGPT

The BloombergGPT LLM is a 50-billion parameter model that has been trained on a wide range of financial data that includes a mix of proprietary finance data and general-purpose data. Thus, this model is capable of performing NLP-specific tasks in the finance industry. Some of the popular use cases of this model are financial analysis, research, automated financial reporting, financial data processing, and financial sentiment analysis.

2. FinBERT

This is an open-source NLP model that is trained to analyze the sentiment of financial text. It is built by further training the BERT language model in the finance domain. It has been trained using a large financial corpus, thereby fine-tuning it for financial sentiment classification.

3. FinGPT

This is another open-source LLM model for the financial sector. It follows a data-centric approach through which researchers and practitioners can access resources to develop their Financial LLMs. FinGPT has a high potential to be used for Rob-advisor, financial sentiment analysis, credit scoring, fraud detection, portfolio optimization, risk management, and others.

Also Read: Large Language Models in Healthcare: Innovating Patient Care

Challenges and Considerations in Implementing LLM in Finance

Though there are a myriad of use cases and benefits of LLM in the finance industry, the associated challenges or considerations can’t be ignored. Here are the most imperative challenges of implementing LLM in finance:

a. Assessing Accuracy:

LLMs are black-box AI systems that leverage a deep-learning subset of AI to understand and generate new text. Popular models such as Google’s PALM or OpenAI ChatGPT are built for general tasks like “writing a letter” or “writing a poem.” Since LLMs are highly language-based and non-deterministic in nature, it becomes difficult to compare and predict the output. That’s why the evaluation of these models is also difficult.

Accuracy testing frequently uses evaluation measures like BLEU (Bi-Lingual Evaluation Understudy) and ROUGE (Recall Oriented Understudy for Gisting Evaluation). You can also use benchmark datasets to compare model performance. These benchmarks will help you measure parameters like engagement, task recognition, relevance, and specificity. If these factors are high, then the LLM is good. However, they are not specific to any particular industry.

Furthermore, LLMs are also prone to hallucinations, which refer to the generation of nonsensical or unrelated text, plus brittleness, i.e., high variation in output by small variation in input. That’s why a relevant benchmark and evaluation framework is important for the adoption of cases in which consistency and accuracy are a top priority.

b. Legal Considerations:

The applications of LLMs have raised concerns about the viability of these models as safe and reliable tools. The major legal considerations include how LLM reaches its outcomes, i.e., explainability of the output. Also, it is important to establish the guiding principles and specific rules by which they operate, with a focus on protecting the privacy and personal rights of individuals.

The financial institutions need to be confident that LLMs operate impartially, generate results that can be explained, and won’t overlook crucial information that could influence a choice or result. To make sure they adhere to any legal requirements imposed on Automated Decision-Making systems (ADMs), we conduct thorough bias testing on the LLMs we train and deploy for every use case. After they are finished, we can focus on stress testing them to make sure they function as planned—that is, taming the beast.

c. Cost Considerations:

Cost is always a big question for financial players, considering ROI. There are various different direct and overhead costs involved in the LLM development. The major costs are for hosting, inference, training, and the salary of skilled resources who will develop the model. The costs are like an inverted triangle in which hosting costs are at the bottom as they are mostly definitive in nature, and training is at the top due to wide cost variance.

The hosting and inferencing costs depend on the size of the model – the number of parameters, and how frequently the model is used for inferencing. The larger the model, the higher its costs will be.

The training costs vary greatly from hundreds or perhaps thousands of dollars for parameter-efficient fine-tuning (PEFT). The majority of businesses use some kind of PEFT, which drastically reduces expenses by fine-tuning only a portion of the initial weights or a few more weights.

Read More: LLM for Software Development: Faster & Smarter Coding

Future of LLM in Finance

We are expecting significant development and more use cases of LLMs in the coming years in the finance and banking sectors. Here are some of the exciting trends:

a. Improved Customer Service:

The future of chatbots in the finance industry will cross the threshold of accuracy. Now, the conversation will be human-like, and even a customer with limited financial knowledge can be guided by an LLM-powered chatbot for tasks like how to open a credit card account.

b. Automated Compliance:

There will be various routine tasks in the finance industry that can be automated with the integration of other technologies like Robotic Process Automation (RPA) with LLMs. In addition to freeing up valuable staff time for more strategic duties, automating compliance greatly lowers the likelihood of errors that can arise with old manual techniques.

c. Integration with Blockchain:

Blockchain is already establishing its roots in the finance industry by making transactions faster and more secure. The integration of LLM in blockchain will help audit in real-time, helping the models adhere to the industry’s compliance standards. LLM and blockchain combination can also help in detecting fraud.

Wrapping Up

Currently, LLMs are still in the growing stage, and as we can see from the statistics, this technology has vast potential. In the finance industry, we can expect huge traction from the LLMs in varying use cases. Thus, investment in this technology holds a high ROI and great potential.

At Octal IT Solutions, the best banking and finance app development company, we can help you develop applications based on LLM technology to meet your business objectives and goals. Our developers have vast experience in developing varied fintech solutions. Let us know your requirements.

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