In the finance world, conversational AI in banking and fintech is quickly transforming how institutions communicate, operate, and serve customers.
VELLIS NEWS
10 Jul 2025
By Vellis Team
Vellis Team
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By merging natural language processing, voice recognition, and machine learning, conversational AI enables banks and fintechs to deliver real-time, interactive experiences that feel human, even when they’re not. From automating customer support to flagging fraudulent activity, conversational AI is making financial services more accessible, efficient, and secure.
In this guide, we’ll explore what conversational AI is, how it’s being used in the financial sector, and why it matters for the future of digital banking.
At its core, conversational AI refers to systems that use natural language processing (NLP), machine learning (ML), and voice or text-based interfaces to simulate human-like interactions. You’ve likely encountered it through chatbots, virtual assistants, or even automated phone agents.
In the context of banking and fintech, conversational AI for finance enables users to perform tasks like checking balances, transferring funds, applying for loans, or even receiving investment advice, all through natural conversation.
Instead of navigating through static menus or complicated apps, customers can now engage in real-time dialogue – whether by voice or text – with AI that understands and responds intelligently.
Conversational AI has found its way into nearly every touchpoint of financial services. Here are some key use cases:
Chatbots are now frontline agents for many banks, answering common questions about account balances, transaction history, or lost cards. Virtual assistants reduce hold times and provide instant answers.
Conversational AI tools analyze spending patterns and recommend budgeting strategies or savings plans in plain language, offering customers more value and insight into their finances.
Instead of endless paperwork and back-and-forth calls, customers can apply for loans or mortgages through a virtual assistant that guides them step-by-step.
Customer onboarding is faster with conversational interfaces that collect documents, verify identity, and complete compliance checks efficiently.
AI-powered bots can notify users of suspicious activity, ask real-time verification questions, and escalate the issue when needed, integrating smoothly with machine learning for fraud detection systems.
Real-world examples are Bank of America’s “Erica,” who helps customers manage their finances via voice and chat, while newer neobanks are building entirely AI-powered customer interfaces.
There’s a reason this technology is gaining momentum: conversational AI in finance delivers measurable improvements across operations:
So what makes all this possible? A blend of advanced technologies:
Deployments range from simple text-based chatbots to multimodal systems that support text, voice, and visual elements across platforms.
As promising as it is, conversational AI does face roadblocks:
To make the most of conversational AI, banks and fintechs should follow a structured implementation path.
We’re just scratching the surface. Here’s what’s coming next:
Tools like Vellis’ payment processing service are also integrating conversational AI into back-office operations to enhance merchant onboarding, payment issue resolution, and transaction insights.
Meanwhile, interactive tools like QR code payment systems are also converging with conversational platforms, allowing customers to initiate payments, get receipts, and resolve issues, all within the same dialogue flow.
From guiding customers through loan applications to flagging suspicious activity and offering budget tips, conversational AI is redefining what financial support looks like in a digital-first world.
It refers to the use of AI-powered chat interfaces to provide financial services and support via voice or text.
Chatbots follow predefined rules, while conversational AI can interpret intent, context, and natural speech patterns dynamically.
Yes, through real-time alerts, behavioral monitoring, and automated identity verification.
Handling of personal data must align with financial regulations like PSD2, GDPR, and PCI DSS.
They use labeled conversation datasets, intent classification models, and continuous feedback to refine accuracy.
Accenture. (2020). Banking Technology Vision 2020: We, the Post-Digital People. https://www.accenture.com/us-en/insights/banking/technology-vision-banking
McKinsey & Company. (2021). AI Bank of the Future: Can Banks Meet the AI Challenge? https://www.mckinsey.com/industries/financial-services/our-insights/ai-bank-of-the-future
IBM. (n.d.). What is Conversational AI? https://www.ibm.com/cloud/learn/conversational-ai
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