Don’t Confuse Your Clients with Jargon, use AI to Boost Financial Literacy

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Krystian Bergman

Jan 31, 2025 • 17 min read
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Lost in financial jargon, customers hesitate, disengage, or walk away. What if AI could turn confusion into clarity—boosting trust, engagement, and conversions?

Picture this: A customer sits at their kitchen table, scrolling through a 40-page agreement for a financial product. Words like “amortization schedule” and “variable APR” leap off the page, leaving them confused and overwhelmed. Unsure of what they’re signing, they either proceed hesitantly—eroding trust in your bank—or abandon the process entirely, taking their business elsewhere.

Financial jargon, while necessary for compliance, often creates barriers for customers who just want clear, actionable information. Confusion leads to disengagement, mistrust, and ultimately lost opportunities—for both your customers and your institution.

AI can help bridge this gap. With tools like chatbots, customers can ask questions while reviewing agreements and get instant, simple answers in real time. Whether it's breaking down loan terms or explaining fees, these tools make complicated concepts easier to grasp.

By making financial language more accessible, banks can improve trust, empower customers to make informed decisions, and build stronger relationships.

The problem with financial jargon

Research from PwC shows that 91% of customers are more likely to buy from a company they trust. But trust doesn’t happen by chance—it’s built through clear, open communication. When customers feel informed and confident in their decisions, they’re more likely to engage with your services, stay loyal, and explore additional products.

The problem is that financial language often works against this trust. According to the Global Benchmark Report 2024 by Smart Communications , 67% of customers are likely to abandon an interaction with their bank or financial services company if the way they collect information is too difficult. Additionally, less than half (47%) of financial services customers rate the communications they receive as very good or excellent, while 9% consider them poor or not good at all.

As financial services move further away from in-person interactions, prioritizing high-quality, clear, and accessible customer communication becomes essential. By breaking down barriers with simpler language and intuitive processes, banks can fundamentally change how they connect with customers.

How AI assistants can help simplify finance

Traditional support channels, such as call centers, often fall short of meeting customer needs, with long wait times and inconsistent responses leading to frustration. AI-powered assistants provide an effective solution by streamlining communication and enhancing transparency.

AI assistants simplify finance in several ways:

  1. Being Transparent: AI assistants provide accurate, consistent answers by pulling directly from updated sources like terms, pricing schedules, or promotional materials. They can also cite specific sections of documents and link back to relevant materials, ensuring customers always have a reference point.
  2. Being Clear: AI tools can simplify financial jargon. Acting as UX writing assistants, they rewrite dense documents into plain, accessible language. By breaking down complex concepts and providing practical examples, AI ensures customers understand their options.
  3. Always-On Support: Available 24/7, AI assistants handle a wide range of inquiries, from basic account information to detailed product explanations. By automating common requests, they reduce the workload on call centers and improve response times for customers.
  4. Personalized Assistance: Unlike static FAQ pages, AI assistants engage dynamically with users, asking follow-up questions to provide tailored answers. For example:
  • When a customer asks about a mortgage, the chatbot could start by asking: “Are you interested in a fixed or variable interest rate?”
  • Based on their response, the assistant could explain the differences: “A fixed rate means your monthly payments remain the same, while a variable rate could change over time based on market conditions.”
  • It might then show estimated monthly payments, tailored to the customer’s loan amount and interest rate preference, and even link to an affordability calculator to explore further.
  • Finally, the assistant could provide a link to application forms or schedule a follow-up with a human advisor if needed.

5. Seamless Document Handling: AI doesn’t require specialized infrastructure to work with existing documents. It can extract information directly from PDFs, such as agreements or policy documents, and link back to them as a source of data.

6. Integration with Banking Systems: AI assistants can connect seamlessly to internal databases, allowing them to provide real-time updates on fees, policies, or promotions. This ensures customers always receive the most current and relevant information.

Real-world examples: how AI simplifies financial jargon

To demonstrate how AI assistants can make complex financial language accessible, let’s look at some “before and after” comparisons. These examples highlight how rephrasing technical terms can transform confusion into clarity:

  • Before: “Expense ratio”
    After: “This is the percentage of your investment used to cover management fees and operational costs.”
  • Before: “Amortization schedule”
    After: “This is a breakdown of your loan payments, showing how much goes toward interest and how much reduces the loan balance over time.”
  • Before: “Variable APR”
    After: “This means your annual interest rate can change over time based on market conditions or other factors.”
  • Before: “Early repayment penalty”
    After: “This is a fee you might pay if you pay off your loan earlier than agreed, as the lender misses out on some interest payments.”

These comparisons make it clear how AI can rephrase technical language into straightforward explanations. By doing this, AI tools help banks improve communication and reduce the friction that often comes with financial decision-making.

Real-world examples of AI in financial education

AI-powered chatbots are transforming how banks communicate complex financial information, making it more accessible and understandable for customers. Here are some notable examples:

HSBC: MOBA virtual assistant

HSBC’s MOBA virtual assistant is designed to help customers navigate banking services, but its approach is more structured than conversational. Instead of open-ended responses, MOBA operates through a series of pre-set options. Customers start by selecting a broad category like “Products” or “Accounts,” which then leads to more specific choices such as “Mortgage Information” or “Managing My Account.”

Rather than directly explaining financial products or terms, MOBA primarily acts as a navigation tool. It guides users to relevant webpages, phone numbers, or app features where they can find more information or take the next steps—such as booking a mortgage appointment or uploading documents. This structured approach ensures accuracy and compliance but limits the chatbot to directing users to external resources rather than engaging in real conversations.

HSBC, MOBA virtual assistant screen 1

Santander: Sandi Chatbot

Santander’s Sandi chatbot helps customers find relevant banking information by offering a selection of predefined query categories, such as “I need help logging in” or “Help getting started.” Rather than directly explaining financial products, Sandi analyzes the customer’s query and redirects them to the appropriate webpage on Santander’s site.

For example, if a customer asks about getting a mortgage loan, Sandi provides a link to the Santander New Mortgage Customers page, where they can find details on mortgage types, eligibility criteria, terms and conditions, and ways to contact a representative for further clarification.

If Sandi is unable to resolve a query, it escalates the conversation to a human customer assistant within the same chat interface, ensuring that customers receive the necessary support when AI alone isn’t sufficient

Santander Chatbot

ING Wholesale Banking: Bill and Regional AI Assistants

ING Wholesale Banking offers Bill, a chatbot designed specifically for corporate clients, available within the InsideBusiness Portal. Bill assists users by answering common banking inquiries and processing requests with natural language processing (NLP) to better understand customer queries. If a question goes beyond Bill’s capabilities, the chatbot transfers the conversation to a human representative.

In addition to Bill, ING has developed regional AI assistants for retail banking customers in select markets. These include Inge (Netherlands), Marie (Belgium), and Lionel (Australia), each designed to interact with customers via channels like Facebook Messenger or ING’s website. These chatbots focus on answering routine banking questions, such as locating ATMs or troubleshooting card issues.

Inga chatbot ING

Outside these regions, ING does not offer chatbot services, instead directing customers to its "Contact and Support" webpage for assistance.

BNP Paribas: NOA Virtual Agent

BNP Paribas’ NOA (NextGen Online Assistant) is an AI-powered chatbot available 24/6 on NeoLink, the bank’s main portal for Securities Services clients. Designed for institutional clients in the UK, US, Jersey, Ireland, Colombia, and Brazil, NOA is not available for retail banking customers.

Unlike many chatbots that rely on clickable options, NOA engages in natural, text-based conversations using machine learning and natural language processing (NLP) to understand customer queries in clear, everyday language. This makes interactions feel more human-like and intuitive.

Retail banking customers do not have access to an AI assistant and must use BNP Paribas' Contact & Support webpage for assistance.

Steps to implement AI for financial literacy

Effectively implementing AI to improve financial literacy requires a structured approach, from planning to deployment. Here are the essential steps:

  1. Align AI with Business Strategy
      • Identify key business objectives and areas where AI can deliver value.
      • Use a discovery workshop (like AI Primer) to uncover opportunities, such as enhancing customer support, automating document generation, or personalizing promotional emails.
  2. Start with Customer Goals, Not Technical Requirements
    • Identify customer objectives, such as applying for a loan or understanding complex financial terms.
    • Determine where AI can add value, such as:
      • Implementing a chatbot to guide users through challenging aspects of the process.
      • Providing AI-powered callouts to explain technical terms and phrases clearly.
    • Define high-level requirements based on these goals before moving to low-level technical details.
  3. Analyze Data
    • Define functional and technical requirements for the AI assistant.
    • Conduct a comprehensive analysis of available data sources, such as terms and conditions, fee schedules, and promotional materials.
    • Design the architecture for the solution, including integrations with existing systems and real-time data access.
    • Develop a concept for guardrails to ensure transparency, security, and accuracy in chatbot responses.
  4. Develop and Build the Prototype
    • Start small with a Proof of Concept (POC) or pilot implementation to test the feasibility of your AI assistant and gather initial feedback.
    • Implement a prototype chatbot powered by advanced language models (LLMs).
    • Create a knowledge base by structuring data from banking regulations, fee schedules, and product promotions.
    • Build interactive features, such as follow-up questioning and response logic, to ensure personalized customer support.
    • Integrate guardrails to prevent the chatbot from providing incorrect or sensitive information.
  5. Verify Through Research and Testing
    • Conduct user tests with individual customers and bank employees to evaluate the chatbot’s responses and overall functionality.
    • Analyze the quality of answers, the system’s ability to understand user needs, and its effectiveness in simplifying financial concepts.
    • Collect feedback on usability, user experience (UX), and feature requests to guide further improvements.
  6. Test Accuracy, Logic, and Security
    • Ensure that the chatbot delivers correct answers by validating responses against bank data.
    • Test the functionality of follow-up questioning and logical responses to complex inquiries.
    • Perform security testing to confirm the chatbot’s ability to handle atypical or incorrect questions without compromising data integrity or accuracy.
  7. Deploy and Optimize
    • Gradually roll out the chatbot with a clear plan for scaling, including monitoring performance and resolving any post-launch issues.
    • Use analytics and customer feedback to continuously refine the chatbot, improving its natural language processing (NLP) and expanding its knowledge base.

By following these steps, financial institutions can deploy AI-powered assistants that simplify complex banking concepts, enhance transparency, and empower customers with financial literacy.

Overcoming challenges in adoption

AI has the potential to transform financial literacy, but banks must first overcome several roadblocks. One of the biggest is outdated infrastructure. Many financial institutions rely on legacy systems that weren’t designed for real-time data processing or AI-driven interactions. Banks can address this by using middleware and APIs to connect AI tools with existing platforms, ensuring seamless data flow and accurate customer responses.

Security and compliance add another layer of complexity. With strict regulations like GDPR and CCPA, financial institutions must ensure AI doesn’t expose sensitive customer data or generate misleading information. The key is building AI systems with strong safeguards, performing regular audits, and keeping compliance at the core of development.

Beyond technology, people are a critical factor in AI adoption. Employees may see AI as a threat to their roles, while customers may be wary of trusting automated responses. Shifting these perceptions requires clear communication. For staff, proper training can demonstrate how AI handles routine tasks, freeing them to focus on deeper customer relationships. For customers, positioning AI as a support tool—not a replacement for human expertise—can reinforce trust and encourage engagement.

Rolling out AI too quickly can also backfire. A phased approach, starting with a pilot program, allows banks to test its impact, refine responses, and demonstrate its value before a full-scale launch.

How the Accessibility Act drives inclusive banking with AI chatbots

The European Accessibility Act (EAA), set to take effect in 2025, calls on businesses, including banks, to make their services accessible to all customers, including those with disabilities. AI-powered chatbots can play an important role in helping banks meet these requirements while improving customer engagement and trust.

  • Simplifying Financial Terms: Chatbots can break down complex banking jargon into simple, easy-to-understand language. This ensures that customers of all abilities can comprehend financial products and terms, fostering inclusivity and reducing confusion.
  • 24/7 Availability: AI assistants provide consistent support, empowering customers to navigate banking services at their own pace, regardless of the time or their specific needs.
  • Universal Design: AI chatbots can integrate assistive technologies such as screen readers, voice interaction, and adjustable text size. These features align with the EAA’s emphasis on inclusive design, ensuring digital services are usable for individuals with diverse abilities.

By adopting accessible AI chatbots, banks not only comply with the EAA but also demonstrate a commitment to serving all customers equally. These tools create a more inclusive experience while opening doors to untapped market segments.

Conclusion: bridging the gap with AI

Clear communication is the foundation of trust in banking, yet financial jargon and outdated methods have long made financial services feel inaccessible. When customers struggle to understand complex terms, they disengage—creating missed opportunities for both banks and their clients. AI-powered tools offer a way to change this dynamic.

With chatbots and virtual assistants, banks can simplify financial concepts, provide real-time support, and deliver personalized guidance. By making financial products easier to understand, AI helps customers make informed decisions, strengthening trust and long-term loyalty.

Of course, adopting AI comes with challenges, from integrating with legacy systems to ensuring compliance. But with a thoughtful approach—starting with pilot programs, refining based on feedback, and implementing strong security measures—these obstacles can be overcome. The result is a scalable, effective solution that enhances both customer experience and operational efficiency.

Investing in AI isn’t just about simplifying finance—it’s about creating stronger, more meaningful connections with customers. By making financial services clearer and more accessible, banks can drive engagement, build loyalty, and set a new standard for customer-centric innovation.

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Krystian Bergman

AI Consulting Lead at Netguru
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