Leveraging Large Language Models in Finance

The influence and impact of Artificial Intelligence (AI) and Large Language Models (LLMs) in financial services and banking are growing at an astounding pace that will scale dramatically in years to come.

A noteworthy 2022 Bank of England (BoE) survey reveals that 72% of UK financial services firms now use machine learning, with that percentage expected to rise by 3.5 times over the next three years.

Nearly 60% of respondents to a McKinsey survey indicate they have embedded at least one AI capability. Deloitte also points out that long-term strategic AI incorporation will transform the finance industry as technologies become more accessible, accurate, accessible, and transparent resources relevant, and valuable.

McKinsey states that to compete successfully and thrive, banks must now become “AI-first” institutions, with AI at the foundation of new value propositions and improved customer experiences. Underscoring that statement is a prediction that the potential annual value of AI research and analytics for the global banking sector could soon reach $1 trillion—a figure driven by decreasing data storage and processing costs, increased access and connectivity, and swiftly advancing AI technology.

Natural Language Processing (NLP) and large language models can sharpen and accelerate decision-making while personalizing and simplifying services for customers and employees. Both customer experiences and back-office processes benefit exceptionally, while costs fall thanks to automation, reduced error rates, and more efficient resource utilization.

This spells an exciting moment for financial enterprises, with new opportunities and ideas arising from nearly infinite troves of data. Artificial Intelligence, particularly in the realms of Natural Language Processing and Large Language Models, is poised to revolutionize various sectors.

It will make unprecedented speed and intelligence, omnichannel agility, seamless integration across platforms, and effortless automation of manual tasks possible. Rapid introduction of new features and cutting-edge technologies will spur effective competition, benefit from rising market trends, and keep customers engaged with fresh offerings.

This discussion will cover some swiftly evolving use cases enabled by large language models (LLMs). These models support financial services firms by analyzing the volume of customer data, augmenting human decision-making, extracting accurate and valuable insights from earnings figures and complex reports, conducting sentiment analysis of financial documents, and supporting governance goals and challenges. You can learn more by listening to the LLM Use Cases for Financial Services podcast.

The leap to Large Language Models (LLMs) contributes to an unrivaled ability to streamline finance operations while bringing new offerings to financial markets at unprecedented speed. As mentioned above, they make automation of routine and manual tasks possible, improve overall efficiency and performance, enhance the customer experience, and support firms in a dynamic and competitive landscape.

LLMs offer near-limitless valuable capabilities, including:

Large Language Models trained on real-world financial data from large-scale sources like Bloomberg, US banks, and Capital IQ can guide investors in deciding whether to buy or sell shares. Once the large language model learns how to identify patterns and forecast rises or falls in share prices, it can make these calculations on the fly without needing changes to the code.

In a dynamic financial ecosystem, banks are in unrelenting competition with their peers—as well as disruptive new structures such as FinTechs—to offer popular, simplified services to customers.

Technology is a significant differentiator in this race, giving bold innovators the leading edge in introducing new benefits, optimizing current service offerings, and using new avenues for attracting competitors’ customers while keeping their customers loyal. Financial organizations must position themselves at the forefront of digital transformation to effectively capitalize on LLM advantages.

Overall, the ability of LLMs to perform complex financial calculations and analyze unstructured data makes them valuable tools for financial companies seeking to improve their decision-making processes. Financial institutions can now use large language models to analyze mass data volumes, such as financial reports, and quickly generate summaries and insights that aid in risk assessment.

The value of this generated text enables analysts to make informed decisions quickly and efficiently, and the mass analysis of data improves LLM performance without human intervention. As such, the advent of LLMs has unlocked the door to vastly improved revenue forecasting and stock price prediction.

Fast, scalable, agile computing resources are vital to competitive banking success. That means investing in robust computing resources, the latest hardware, and high-performance cloud technology. These are serious investments, but the ability to process data at higher rates, lower costs, and greater accuracy can make the difference.

Leveraging the right LLM strategy to uplevel customer services and back-end processes gives organizations an edge in building growth. Banks will always need advanced, scalable AI models, robust toolsets, and standardized procedures to build, test, deploy, and monitor models repeatably and reliably.

Banks are constantly under well-publicized scrutiny from regulators, customers, and the public—creating unrelenting pressure to submit timely, accurate reports and financial analyses that meet ever-widening regulatory obligations.

The complex compliance process demands the collection of vetted data from diverse sources and systems. LLMs contribute to this high-stakes activity, automating data collection and improving the speed and accuracy of decisions. This enables a firm to meet compliance obligations—saving fines, avoiding lawsuits, and preserving the brand’s integrity.

AI advances also impact regulations, spurring changes and adjustments to the compliance process. Compliance teams must invest in new technologies that enable financial institutions to keep pace and remain future-ready as new regulations emerge and challenges arise.

The ability of financial LLMs powered by AI to analyze massive volumes of financial data in real-time exposes the growing challenge of fraudulent activities and makes effective fraud detection and protection possible.

With their ability to examine patterns and pick up on unusual or anomalous behaviors, LLMs proactively identify cybercrime threat campaigns at earlier stages and effectively defend the confidentiality and assets of a financial enterprise and its customers. The ability to prevent losses and reduce or preempt attacks is a significant LLM benefit.

Financial firms are tasked with analyzing potential customers, loans, and investments with unremitting accuracy amid towering stakes. Analyzing diverse data sources and utilizing advanced machine learning algorithms, LLMs support precise decision-making and dissect layers of potential risk with speed and efficiency that would not be otherwise possible without dedicating vast amounts of time and personnel to the task.

Powered by LLMs, AI-driven chatbots and virtual assistants provide highly customized, human-like interactions, resulting in pleasant, efficient customer engagement and making self-service convenient and economical.

Conversational agents powered by LLMs can handle an increasingly wide range of customer inquiries and quickly resolve routine queries. Customers expect 24/7 omnichannel service, and LLMs can deliver that.

Onboarding new customers is a one-time opportunity to build a positive relationship. LLMs support both the company and the customer by assisting in the onboarding process, guiding the customer through convenient, simplified account setup, answering their questions, and elevating a conversation to introduce popular, beneficial new services.

With their ability to analyze and forecast market trends, sentiment, and volatility, LLMs can assist investors and traders in making intelligent, informed, and timely decisions. LLMs have instant access to diverse sources of reliable data—news articles in respected journals, vetted expert social media posts, academic and financial reports, noted analysts, and more to help them offer smart advice regarding investment directions.

LLMs can also generate trading signals in near real-time with alerts based on fast-changing financial conditions and international breaking news.

The AI-driven bank of the future faces burgeoning pressures: ruthless competition, and security issues originating from both internal and external sources. These include:

McKinsey notes that tech giants are entering financial services with major advantages. These include precise, granular insights into customers’ preferences, needs, and choices; near-infinite scalability; aggressive embracing of new technologies; deep understanding of AI; and access to low-cost capital. Their ability to gain footholds will pose a significant challenge to financial institutions.

Modernizing bank infrastructure requires substantial capital, time, effort, personnel, testing, and training investment. Cloud computing is the answer to many of these, with its scalability, resilience, improved security, and infrastructure virtualization. The cloud technology platform speeds time-to-market and enables swift setting up of new environments.

A centralized, consolidated data source allows every part of the firm to access the same data in the same way, free from silos, discrepancies, and inaccuracies. With their superior data management capabilities, LLMs can resolve this issue, providing researchers with a 360-degree view across the organization, deeper insights, and advanced analytics.

Traditional banks that take on external partners should be able to reuse code and software internally, as well as integrate data and services across functional siloes to keep services streamlined and efficient for customers.

LLMs can accelerate development, reduce complexity, promote efficient collaboration, and support a positive customer experience during change by streamlining access to current data.

Banking executive leadership bears the responsibility of ensuring that AI-mediated interactions or summaries are accurate and reliable. This process calls for robust privacy and security measures to safeguard customer data. Again, compliance with financial regulations, including anti-money laundering (AML) and Know Your Customer (KYC) laws, is critical.

These should be integral to AI models to minimize risk and maintain trust in the organization and its processes. Implementing LLMs requires prioritizing accuracy, privacy, security, and regulatory compliance, balancing benefits with risks. Robust governance frameworks help LLMs do their work of transforming financial institutions and optimizing the customer experience.

LLMs enrich and benefit the banking experience in the following ways:

In-depth Analysis: LLMs can be like having a private financial consultant who can clarify data complexities. They can translate complex financial data analysis, such as gap studies and forecasting, into clear, comprehensible messages for customers.

Portfolio Management: LLMs support investment-focused banks by analyzing market trends and offering portfolio allocation recommendations. This has the effect of both risk management and helping customers create intelligent investment strategies. Proactive financial advice requires analyzing your income, expenses, and investment options to offer personalized recommendations. LLMs can alert you to an ideal opportunity to invest—or warn you when you’re overspending.

Improved Customer Interactions: LLMs have the potential to enrich and expand relationships among products, customers, and employees. Promoting effective communications leads to new product marketing opportunities like upselling and cross-selling and benefiting customers with new products and services.

Loan Services: LLMs are a valuable element of the loan approval process, whether strict regulations oversee mortgages, personal or business loans, and the approval process. With LLM support, you can leverage domain-specific data to speed up the underwriting process, preserving compliance, risk tolerance, and timeliness.

Research reveals that integrating LLMs can propel productivity and improve the customer experience by a dramatic 40%. Unquestionably, LLMs are transformative for the financial industry and associated careers.

The broad span of capabilities LLMs unleash, plus the swift advance of AI technology create a whole new landscape for finance, a one-time tradition-bound industry. Thanks to LLMs, financial institutions are overcoming their vulnerability to fraud and launching new services and conveniences to empower and delight customers.

Aisera offers a powerful foundational model to buy an Enterprise LLM off-the-shelf called AiseraLLM, however, if enterprises choose to build their own LLM, Aisera does support building on top of popular foundational models like GPT-3.5, GPT-4, PaLM 2, Vertex AI, Falcon and LLaMA1. Each brings its strengths and weaknesses, so your best choice will depend on your specific needs and circumstances. Here are a few considerations:

Choosing an LLM largely depends on the business objectives and use cases it’s specifically designed or intended for. For example, if customer support is a priority, you should prioritize models that offer superior understanding and generation of natural language.

The amount and type of data accessible for training and fine-tuning the model are other factors to consider. Because LLMs must be pre-trained on a large amount of diverse domains of data, the more diverse and higher-quality data you can make available, the better your chances for successful performance.

Latency refers to how quickly a model can generate a response, and depending on your use case and industry, latency can become a pivotal factor. To cite a few considerations for text generation: smaller models with fewer layers and parameters tend to be faster than larger models; however, they may sacrifice performance for speed.

GPUs are generally faster than CPUs for deep learning tasks but can also be more expensive. Increasing batch size can improve throughput but also increase latency. Caching and preprocessing can speed up requests that are repeated or similar in nature.

The cost of implementing an LLM can be significant, especially when considering the computational resources needed for training and inference and ongoing maintenance costs. When deciding, think of potential updates that may be required to the model, and consider the resources available for support and the model’s compatibility with future technologies.

Financial enterprises should consider models that offer robust security features and are fully compliant with relevant regulations.

Selecting the best LLM is a multifactorial and complex challenge, so be sure to research how various models address your unique needs. Taking the time for careful planning will substantially benefit your business operations. To explore this transformative potential of AiseraLLM, we invite you to book a custom AI demo with us.