Jun 25, 2024

AI Banking: A New Perspective to Legacy Systems

32% Banks used AI in 2020 and this number has now increased to 57% in 2023, all thanks to practical importance of these LLM based Conversational AI

AI and Banks, Financial Institutions, Bank, Money, AI, Erica

In this digital age, the banking sector is undergoing a significant transformation and one of the key drivers of this change is the adoption of artificial intelligence (AI), particularly Language Learning Models (LLMs), in the form of chatbots. These AI-powered chatbots are revolutionizing the way banks interact with their customers, providing a more efficient, personalized, and user-friendly service.

What are LLM-based AI Chatbots?

LLM AI-based chatbots are advanced conversational agents that use machine learning and natural language processing to understand and respond to human language. They can handle complex conversations, understand context, and provide accurate responses, making them ideal for customer service applications in the banking sector.

LLM-Based Chatbots vs Traditional Chatbots

Chatbots are already a common feature in many banks, providing 24/7 customer service, answering queries, and even performing transactions. They offer numerous benefits, including reduced operational costs, increased customer satisfaction, and improved efficiency. Now the important question is do LLM base chatbots really have a difference? Let's find out

Traditional chatbots typically rely on pre-programmed responses and lack the ability to understand context, which can lead to inaccurate responses and a poor user experience. On the other hand, LLM-based chatbots use machine learning and natural language processing to understand and respond to human language. For example, a traditional chatbot only answers correctly if the customer says “What’s my account balance” and that's a major problem being solved by LLM-based chatbots. Unlike them, they understand human language instead of just phrases. So when a customer says “Tell my balance” or “Please tell my balance”, all of the queries work and give the same answer.

The Impact of Chatbots in Banking: By the Numbers

Here are some statistics that highlight the impact of AI chatbots in banking:

  1. Increased Lead Generation: Banks have reported six times more leads collected using chatbots compared to traditional lead generation.
  2. Cost Savings: By 2023, banks are expected to save $7.3 billion in operational costs due to the use of chatbots.
  3. Improved Loan Accessibility: Artificial intelligence expands loan accessibility, approving 27% more loan applicants and yielding 16% lower interest rates.
  4. Rapid Adoption: In 2021, The Fintech Times reported that the percentage of midsize banks and credit unions using chatbots tripled in a single year, leaping from only 4% to 13%.
  5. Customer Satisfaction: HDFC’s Electronic Virtual Assistant (EVA) has responded to more than 5 million inquiries with at least 85% accuracy.

These figures demonstrate the significant benefits that AI chatbots bring to the banking sector, including cost savings, increased efficiency, and improved customer service. As AI technology continues to evolve, we can expect these benefits to grow even further.

Case Studies: LLM based Conversational Agents in Action

Several banks have successfully implemented chatbots and are reaping the benefits. For example:

  • Bank of America’s chatbot, Erica, has over 10 million users and has handled more than 100 million client requests. Erica helps customers with tasks like checking balances, scheduling payments, and providing credit report updates.
  • Wells Fargo’s chatbot, which is integrated with Facebook Messenger, allows customers to check their balance, request their transaction history, and locate ATMs. The chatbot uses AI to understand natural language, making it easy for customers to interact with it.

The Future Outlook

The future of LLM AI chatbots in banking looks promising, with continuous advancements in AI technologies. As these chatbots become more sophisticated, they will likely take on more complex roles in banking, further enhancing operational efficiency, customer service, and risk management.


LLM AI-based chatbots are transforming the banking industry by enhancing customer service, reducing operational costs, personalizing services, and improving risk management. While challenges exist, the potential benefits make these chatbots an invaluable asset in the banking sector's ongoing digital transformation.

Decision pointsOpen-Source LLMClose-Source LLM
AccessibilityThe code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation.The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer.
CustomizationLLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques.Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer.
Community & DevelopmentBenefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements.Development is controlled by the owning company, with limited external contributions.
SupportSupport may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance.Typically comes with dedicated support from the developer, offering professional assistance and guidance.
CostGenerally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance.May involve licensing fees, pay-per-use models or require cloud-based access with associated costs.
Transparency & BiasGreater transparency as the training data and methods are open to scrutiny, potentially reducing bias.Limited transparency makes it harder to identify and address potential biases within the model.
IPCode and potentially training data are publicly accessible, can be used as a foundation for building new models.Code and training data are considered trade secrets, no external contributions
SecurityTraining data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the communityThe codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment
ScalabilityUsers might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resourcesCompanies often have access to significant resources for training and scaling their models and can be offered as cloud-based services
Deployment & Integration ComplexityOffers greater flexibility for customization and integration into specific workflows but often requires more technical knowledgeTypically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor.
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