In recent years, the banking sector has started recognizing the importance of innovation and aligning its strategies with evolving technologies. This recognition is driven by the realization that adaptability is key to maintaining competitiveness in the market. This is not the first time where banking sector has undergone a shift in its operational approach. Back in 2008, banking institutes migrated their cloud-based infrastructure into implementing automation tools and blockchain technology and witnessed a significant shift of technological excitement in a short timeframe.
However, nothing has garnered as much excitement as the introduction of Generative AI (Gen-AI) or more precisely Large Language Models (LLMs) which come as enormous transformative potential for banking institutes.
What do LLMs mean to Banking Sector?
Even before the Generative AI launched, banks experimented with AI tools for some time. Unlike traditional AI systems, LLMs are designed to produce outputs possessing a human-like touch, driven from a huge repository of complex data. LLMs are AI models developed to understand and create human-like content based on a given input.
Large Language Models (LLMs) comprise of large number of parameters ranging from millions to billions allowing models to understand and learn from vast amounts of data. Guiding by machine learning (ML) generates predictive text, and can be trained to summarize answers, translate language, and other applications. They can process and generate natural language contextually relevant text, which came out as a game-changer.
LLMs can definitely deliver personalized customer experience, enriched with insights drawn from past data such as customer behavior, and prior interactions. LLM can stimulate vast amounts of data in real time and provide informed and human-like responses to serve customers more effectively. LLMs can have great applications in creating meaningful human-touch conversations i.e., virtual assistants, and chatbots to provide customers comprehensive support with some common queries, freeing up human agents from daily tasks and enabling them to devote their efforts to more strategic endeavors.
How can Banks use LLM?
The application of LLMs is not just limited to enriching customer experience through virtual assistants or chatbots. The sector started witnessing the exposure of wider use cases of LLMs that are transforming traditional labor banking processes and interactions. Major front operations from onboarding customers and KYC to account management, risk assessment, and compliance can be automated using LLMs, resulting in an overall increase in efficiency. Theoretically, applications of LLMs are endless, some standout use cases include:
Automate Daily Operations
LLMs can be used to create predefined templates for various financial documents such as account openings, loan applications, or invoices just by fetching information to populate responses. This definitely reduces lengthy processes i.e., onboarding customers, reducing human errors and interaction, and increasing overall customer experience and satisfaction.
Conducting Financial Research and Analysis
LLMs are capable of scanning and analyzing enormous amounts of publicly available data such as news reports, company internal repositories, social media content, videos, and historical trends to give comprehensive insights to analyst teams and investors. They can create research reports, forecast potential trends, and summarize investment prospects, personalizing financial recommendations and counsel.
Detecting Fraud and Suspicious Behavior
LLMs can analyze a fairly large volume of customer databases and transaction history to raise credit risk assessment, detect and report any fraudulent activities, and identify suspicious behavioral patterns. Several banks like J.P. Morgan, HDFC Bank, Axis Bank, and many others have deployed or are in the process of deploying LLMs for fraud detection and improved security.
Onboard and Train In-house Teams
One more undiscussed application of LLM models is team onboarding and training. Banks undergo multiple levels of employee onboarding followed by their training. Finding qualified candidates and educating them on organizational operations is still a challenge that directly impacts productivity and overall growth rate. The in-house operational team contributes the most to the growth cycle and hence they must be well-trained and educated about the services, operations, and daily tasks.
For training in-house teams, there are multiple LLM models, like Salevant.ai trained specifically on the organizational internal knowledge repositories to disseminate the information and insights till the last mile of a team. A trained model can be easily accessible by analysts, researchers, and customer-facing teams so they can find valuable data points of the organization required for their operations. Effective knowledge dissemination among in-house employees reduces training time and makes the team more aware and productive when they have insights at their tips.
How LLMs are different from NLG applications?
Natural Language Generation (NLG) is gaining popularity in recent times because of its capability to generate human-like texts from the input provided. However, LLM is far more complex when compared with NLG systems and capable of capturing a wider range of language patterns, nuances, and subtleties. LLMs are slightly more adaptive, fetching insights from a wider range of sources to adapt to different language-related applications and tasks. With ongoing data training and access to fresh, relevant data, LLMs can continuously improve their output generation capabilities.
NLG technologies, on the other side, rely on template-based and rule-based systems, based on predesigned templates, making them less adaptable or flexible, limiting their creative capabilities in their responses. NLGs excel in maintaining accuracy and consistency while generating structured content, which makes them valuable in areas where templated are valuable such as financial reporting, and transaction reporting. However, they require data preprocessing, which may be labor-intensive, making them less effective while handling unstructured content. LLM overall, offers greater adaptability and a much broader range of applications than NLG technologies.
Can LLMs help the Banking and Finance functions?
LLM models can be deployed in conventional banks to streamline processes across many functions including Finance functions, customer onboarding, and in-house training. On a large scale, they can help summarize complex finance information in a structured manner - necessary while compiling narrative sections of financial reports. LLMs trained with internal knowledge repositories can simplify the financial data retrieval process for reporting and support purposes, allowing the in-house team to ask natural language queries and get their answers from the financial database. Their capability of extracting insights and valuable data from databases such as historical accounts records, customer profiles, or financial reports can be utilized to generate immediate financial statements with accuracy, saving time and minimizing human errors.
With effective knowledge dissemination among the in-house teams and departments, LLMs can also supervise changes in financial regulations or terms & conditions and ensure each team member is well aware of the updates and the upcoming requirements that can impact financial reporting. Interestingly, LLMscan plays an important role in planning, forecasting, and budgeting by understanding historical financial data and trends to create forecasts for revenue, cash flows, expenses, etc. letting professionals make more informed and data-driven decisions. LLMs can develop various scenarios based on multiple assumptions, enabling banks to prepare for a broader range of potential outcomes.
Conclusion
From the discussion, it is clear that LLM applications are not just limited to language generation, but can be deployed in many functional areas of banks to ease a variety of applications and tasks with accuracy. Deploying LLMs can be a complex task; the choice of platform for deployment depends on various factors such as the bank’s size, in-house team members, tech infrastructure, and budget. However, some banks opted for cloud-based service providers to deploy the LLM model, which offers more scalable and measurable solutions.
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