Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
Industries where customer service is a top priority face increasing costs due to the demand for excellent customer service. Banking chatbots enable customers to complete transactions via voice or text, reducing operational costs and enhancing customer satisfaction.
As of 2026, Bank of America’s virtual assistant Erica processes 2 million daily consumer interactions, saving the bank the equivalent of 11,000 staffers’ daily work.1 The bank is investing $13 billion in technology across every line of business in 2026, with AI and machine learning spending increased 44% over the past decade. Erica has evolved beyond a “beefed-up chatbot” to become a pain-point resolver, seamlessly connecting customers across channels without requiring re-authentication. The system is expanding from retail banking to support business customers as well.
We have compiled the top 7 chatbots with financial literacy, including their features, comparisons, and best practices for deployment to address cost and service concerns.
*Sorting is based on the average rating.
Tidio can handle routine banking inquiries, provide basic financial information, and support small to medium-sized banking institutions and credit unions with their customer service needs.
Key features:
Figure 1. Tidio’s banking chatbot.2
Boost.ai is a conversational AI platform for financial services, especially with a strong presence in European banking. It handles regulatory inquiries, performs complex financial calculations, and manages sensitive customer data in compliance with banking standards.
Key features:
Industry Recognition: Boost.ai was named a Leader in the 2025 Gartner Magic Quadrant for Conversational AI Platforms, validating its position as a top-tier enterprise conversational AI solution.3 The platform launched on AWS Marketplace in July 2025 and formed strategic partnerships with SwitchThink to deliver GenAI agents for credit unions and with Ciklum to expand enterprise access to conversational AI.
Intercom is a customer engagement platform designed for banking applications, targeting digital-first financial institutions. It emphasizes banking customer engagement, enhances digital banking experiences, and assists with financial product adoption and customer retention.
Key features:
IBM WatsonX Assistant is now part of the broader WatsonX Orchestrate ecosystem, which brings all AI agents together for multi-agent orchestration.4 The platform emphasizes “no rip and replace” integration, allowing banks to bring agentic AI to current workflows, automations, and apps without vendor lock-in. Watsonx Orchestrate supports hybrid deployment across cloud and on-premises environments, meeting security, compliance, and data residency needs for regulated banking environments.
Key features:
Figure 2. IBM’s visual chatbot builder demo page.5
Yellow.ai’s BFSI platform is a comprehensive AI solution designed for the Banking, Financial Services, and Insurance industries. It understands the complexities of financial products, manages compliance-sensitive interactions, and automates workflows specific to banking.
Key features:
Figure 3. Yellow.ai’s AI and human agent blended service.6
LivePerson Conversational Cloud is an enterprise-grade conversational AI designed for banking, with various implementations and partnerships. It detects urgency levels, escalates sensitive financial matters properly, and preserves context across different banking channels.
Key features:
Figure 4. LivePerson’s banking chatbot’s Fraud prevention.7
The platform is specifically designed for financial services, leveraging extensive banking domain knowledge and trained on banking terminology, regulatory standards, and financial procedures.
Key features:
Figure 5. Kasisto KAI’s Agent Assist.8
Oracle Financial Services launched an enterprise-class agentic AI platform specifically for banking with pre-built AI agents and multi-agent orchestration.9 The platform moves beyond task automation to deliver business intelligence, agility, and trust at scale.
Key features:
Chatbots can engage with visitors on the bank’s digital platforms to generate leads and assess those leads with relevant questions.
Example: After a customer completes a transaction on a bank’s mobile app, the chatbot initiates a brief conversation asking for feedback. Instead of filling out a long survey, the customer answers a few questions conversationally, making the feedback process more engaging and less time-consuming.
24/7 availability and the tireless and consistent nature of chatbots for customer support are important advantages for chatbots in banking.
Long feedback forms and surveys can be a nuisance to complete. A chatbot can engage customers with its natural language understanding and generation.
Example: After a customer completes a transaction on a bank’s mobile app, the chatbot initiates a brief conversation asking for feedback. Instead of filling out a long survey, the customer answers a few questions conversationally, making the feedback process more engaging and less time-consuming.
Customers’ conversations with chatbots can be analyzed to personalize the bank’s messages for the customer.
Example: A customer frequently interacts with a bank’s chatbot to ask about mortgage rates. The bank analyzes these conversations and sends personalized emails with information on mortgage products, rates, and offers that match the customer’s interests.
The next wave of banking AI moves beyond answering questions to offering guidance during moments of customer uncertainty, particularly in high-stakes financial decisions.10
Example: When customers open their banking app, facing life-shaping decisions, buying a home, managing debt, handling cash-flow stress, or planning retirement, AI systems can interpret context, understand their financial history and goals, and explain options in plain language. These systems synthesize complex information to help guide important decision-making while providing the reassurance, clarity, and confidence customers need during emotional financial moments.
Specify the needs for your banking chatbot: Start by identifying the particular requirements of your organization and establishing definite objectives for success. Consider these critical deciding factors:
Evaluate platforms based on your specific banking needs. You can request detailed demos tailored to your main use cases from most vendors. Some aspects you might ask vendors to demonstrate include:
Work with your IT team and vendor specialists to integrate the chatbot.
After completing the technical integration, deploying a chatbot is similar to deploying any other chatbot.
You should train your chatbot with relevant data and design conversation flows that match your institution’s service standards through conversation design, knowledge base development, and your brand’s preferred voice and tone. Check out how to build a chatbot.
Then, prepare your team for the changes the chatbot will bring and train your agents to maximize efficiency. Afterward, you can launch your chatbot and monitor its performance. One of the most important practices is to test continuously and closely monitor the chatbot to optimize its performance.
Recent adversarial testing of 24 AI banking chatbot models from major providers revealed that every model proved exploitable, with success rates ranging from 1% to over 64%.11 Testing revealed “refusal but engagement” patterns where chatbots claimed “I cannot help with that” yet immediately disclosed sensitive information anyway. This underscores the critical need for robust security measures beyond relying solely on the GenAI provider’s guardrails and refusal messages. When a chatbot provides incorrect guidance or misleads a borrower about their dispute rights, regulators treat it as a compliance failure, not a technology experiment.
Banking chatbots manage sensitive financial data that demands the highest security standards. Here are some measures you can implement in your chatbot to ensure the highest level of customer security.
Put operational protocols in place to guarantee reliable, superior chatbot performance by using:
Use chatbots strategically to improve your institution’s competitive edge by:
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