A banking chatbot frustrates when it covers only 10 to 15 frequent questions, gives generic answers, and loses context across channels. In a study with Mercoplus LATAM in Colombia, 74% would use a generative AI solution that truly resolves, and 38% would switch banks for it. Delto's BLAM already covers 99% of customer and bank cases.
In the middle of the automation era, banks' digital channels still face an uncomfortable reality: many "smart chatbots" keep generating more frustration than solutions. The customer asks a question, gets a generic answer, can't move forward... and ends up at a branch, at the call center or, even worse, at a competitor. Why does this happen? Why doesn't so much investment in automation translate into a better customer experience? We work with banks across the region to answer this question. And we found four main causes that come up again and again. 1. Limited coverage: automating only the basics isn't enough Many banking bots are trained to answer the 10 or 15 most frequent questions. But that's like opening a branch with short hours: it may ease some traffic, but it doesn't cover most real cases. When the bot "doesn't have it covered," the customer gets frustrated and moves to another channel to solve their problem, or simply abandons the task. At Delto we developed the BLAM (Banking Language Action Model) : a banking language model with more than 300 skills already trained to understand and resolve your users' real cases –from card blocks to digital onboarding, complaints or mortgages– without starting from scratch. If you want to see how it works, explore the platform . 2. Standard answers that don't resolve A generic text can close a case in internal metrics. But if the customer can't do what they came to do, the experience is negative. What matters is understanding the context and acting with precision . Our suite with generative AI understands and analyzes queries in depth, detects patterns and can resolve each interaction with concrete actions and up-to-date data. This is how our AI support agent for banking works: it doesn't just understand the customer, it executes what they need. 3. Omnichannel without context is more of the same A user starts the query on the web, then moves to WhatsApp and ends up (if they have the patience) in the banking app. And at each step, they repeat their problem looking for a solution, frustration keeps growing and tolerance keeps shrinking. Their interaction with the bank, beyond not solving their situation, generates negative emotions toward the brand. No ease, no simplicity, no speed, exactly what automation promised to avoid. That's why at Delto we designed a truly omnichannel experience with persistent context : the dialogue state travels with the customer across channels, even authenticated ones, without losing information. Plus, our solution keeps the conversation active in the background: the user doesn't have to leave the app open while waiting for a resolution. 4. Without feedback, there's no real improvement If you don't measure the real experience (CSAT, NPS, FCR), it's impossible to know whether changes work and customers are happy. Many bots stay stuck, with improvement decisions made blindly. At Delto every conversational turn includes metrics: we measure satisfaction, automatically categorize comments and prioritize improvements based on impact. Every sprint is validated with A/B testing to confirm (or refute) hypotheses before moving forward. Do customers hate bots? No, they hate wasting time and feeling frustrated. In a recent study we ran together with Mercoplus LATAM in Colombia, we found that: 39% prefer human service, 24% feel their query is too complex for a bot, 14% had bad experiences with previous bots. But the most interesting part is this: When a generative AI solution that truly understands and resolves is offered, 74% of users would be willing to use it , and 38% would even consider switching banks to access that experience. Experience does matter, and it can scale. Users don't necessarily demand talking to a human. They demand solving their problem without repeating their story , without going through five channels, without waiting. With Delto's BLAM, which already covers 99% of customer-bank cases , your organization can offer that experience today , without building everything from scratch or putting your customers' satisfaction at risk. Every conversation can be an effective solution, with quality and at scale. And this is just the beginning: as McKinsey confirms, the next frontier is agentic AI . So, what's the next step? Let's talk. Want to see how we solved it with other banks in the region? Book a demo and discover how to turn your digital channel into the most powerful asset in your customer service strategy.
Why do banking chatbots frustrate customers? For four reasons: limited coverage (only 10-15 FAQs), generic answers that don't resolve, omnichannel without persistent context, and a lack of measurable feedback (CSAT, NPS, FCR). Customers end up repeating their story across five channels or abandoning the task.
What is Delto's BLAM? The BLAM (Banking Language & Action Model) is a banking language model with more than 300 trained skills that covers 99% of customer-bank cases, from card blocks to digital onboarding, complaints or mortgages, without starting from scratch.
Do customers prefer talking to a human over a bot? Not necessarily. In the study with Mercoplus LATAM in Colombia, 39% prefer human service, but 74% would use a generative AI solution that understands and resolves, and 38% would even switch banks to access that experience.
What should a good banking chatbot include? Four elements: broad coverage of real cases, not just 10 to 15 frequent questions; the ability to execute concrete actions instead of only answering; persistent context across channels so customers never repeat their story; and continuous measurement with CSAT, NPS, and FCR to improve based on data, not guesswork.