As part of the Chatbot Rank 2024 study, we analyzed how banks are redefining chatbot functionality to go far beyond basic automation. Today’s most effective chatbots act as intelligent service layers — guiding users through the app, resolving complex queries, and driving business metrics like activation, retention, and self-service success. In this insight, we’ve compiled the most forward-looking solutions identified in Chatbot Rank: from image recognition and product onboarding to context-aware prompts and wait-time optimization. Each mechanic reflects a shift toward smarter, more user-centered service.
1. Navigation that accelerates task completion
A well-designed chatbot doesn’t just answer questions — it actively facilitates user journeys by directing people to the exact app sections they need. This reduces the number of steps, taps, and decisions required to complete a task, which in turn improves satisfaction and shortens resolution time.
One standout example involves how a banking chatbot handles a user request for transaction history. Instead of offering a generic response or static link, the bot analyzes the intent behind the query and offers two tailored options: (1) a direct link to the full transaction list for quick browsing, and (2) a shortcut to a visual analytics section that breaks down expenses and income in more detail. This contextual guidance empowers users to choose the format that best fits their need — whether it’s a quick check or a deeper financial review.
2. Image recognition to reduce transfers to human agents
Advanced chatbots are beginning to support image recognition capabilities that allow them to analyze screenshots sent by users — a major step toward more intelligent, automated support. When a user uploads an image, such as a transaction page or error message, the chatbot interprets the visual content to understand the issue without requiring a detailed written explanation. This is especially useful in cases involving unclear charges, cashback discrepancies, or unexpected deductions, where users often rely on screenshots to describe the problem.
Once the image is analyzed, the chatbot can offer context-aware suggestions — such as a direct link to commission details, an explanation of a charge, or relevant FAQs. In some implementations, the bot even presents multiple options to ensure the user finds the most helpful answer. By reducing the need for escalation to human agents, this feature improves resolution speed, increases automation rates, and enhances the user experience for more complex, visually-driven queries.
3. Contextual awareness based on recent activity
Proactive chatbots are increasingly using behavioral signals and recent user activity to anticipate needs and deliver more relevant assistance. Rather than waiting for users to initiate queries, these bots monitor in-app interactions — such as which cashback categories were selected or which products were recently viewed — and surface timely suggestions. For instance, if a user has just chosen new cashback options, the chatbot may remind them when the category resets or prompt them to explore related offers.
In another case, the bot detects recent actions like card blocking or browsing insurance products, and responds with targeted prompts — such as help with reissuing a card or providing details about relevant insurance plans. This kind of context-aware assistance not only feels more intuitive and personal but also increases the chances of users engaging with the chatbot and completing tasks. The result is a smoother journey, higher conversion rates, and a stronger sense of digital support that adapts to user intent in real time.
4. Option to cancel the operator transfer
Maintaining user control during support interactions is key to a positive experience. One leading chatbot introduces a smart feature: after a user requests a human agent, a brief countdown timer appears with an option to cancel the transfer by tapping “No chat needed.” This small window gives users a chance to reconsider if the issue was resolved in the meantime, promoting transparency and reducing unnecessary escalations.
This mechanic could be further enhanced by extending the cancel option into the waiting period — allowing users to exit the queue if they change their mind or find the answer elsewhere. By giving users control at multiple points, the system not only decreases drop-offs and frustration but also optimizes support resources by minimizing idle or abandoned handovers.
5. Prioritization based on urgency
Effective support isn’t just about speed — it’s about smart prioritization. One bank’s chatbot intelligently distinguishes between critical and non-critical user queries. Urgent issues, such as fraud alerts or payment failures, are flagged and routed immediately to a human agent. In contrast, less time-sensitive requests are placed in a queue with a clear status update, ensuring that resources are allocated where they’re needed most.
To add flexibility, the chatbot includes an “Issue resolved” button that users can tap if they no longer need assistance while waiting. This self-cancellation feature helps reduce unnecessary agent load and speeds up the queue for others. By triaging requests and allowing real-time adjustments, the chatbot improves both operational efficiency and the user experience — ensuring faster help where it matters, without making others feel neglected.
6. Setting expectations and reducing perceived wait time
Improving the waiting experience is crucial for reducing drop-offs during support interactions. One effective solution is to display the exact estimated wait time before a human agent becomes available. This level of transparency helps manage user expectations, reduces uncertainty, and gives users a sense of control — all of which contribute to higher satisfaction, even in moments of delay.
To further enhance engagement, the same chatbot offers a simple in-chat game that users can play while waiting. This interactive element not only distracts from the passage of time but also helps retain users in the support channel, increasing the likelihood they stay until an agent joins. Together, wait-time indicators and lightweight entertainment turn passive waiting into a more tolerable — even pleasant — part of the support journey.
7. Enabling product onboarding directly in the chat
Some banks are transforming chatbots into effective sales assistants by enabling users to start product applications directly within the chat interface. In one implementation, the chatbot helps users choose a bank card by guiding them through key decision criteria — such as whether they prefer free servicing, specific benefits, or card type. Once preferences are defined, the bot offers tailored product recommendations and allows the user to proceed with the application flow right in the chat, including selecting the card type and who the card is for.
Although the final steps, such as identity verification or agreement signing, are completed in the mobile app, the chatbot initiates the journey in a way that feels natural and low-effort. By lowering entry barriers and reducing navigation friction, this conversational approach improves user engagement and significantly increases conversion rates. It also reinforces the chatbot’s role not just as a support tool, but as an active driver of digital sales.
8. Collecting pre-chat data to speed up resolution
To reduce idle time and improve support efficiency, some chatbots prompt users to briefly describe their issue before connecting them to a human agent. This simple step keeps users engaged while waiting and ensures that agents receive essential context in advance — speeding up resolution and reducing the need for back-and-forth clarification. In practice, this means a user reporting a failed transaction or blocked card can receive help faster, as the agent can begin reviewing the case before the chat even starts.
More advanced implementations guide users with structured prompts or quick-reply options like “Refund request” or “Unexpected charge,” which improves data quality and enables better routing. This small enhancement not only shortens time-to-resolution but also increases satisfaction by making the support process feel smoother, more personalized, and more responsive — all with minimal effort from the user.
9. Handling objections to prevent product cancellations
An increasingly valuable role for chatbots is in retention-focused conversations, especially when users are about to cancel or downgrade a product. Rather than passively processing such requests, well-designed bots are programmed to intercept exit signals and offer meaningful alternatives that could shift user intent — all without involving a human agent.
For example, when a user attempts to disable SMS notifications, the chatbot doesn’t just confirm the action. Instead, it briefly explains the benefits of the feature — such as real-time fraud alerts or balance updates — helping the user reassess the decision with a clearer understanding of the value. This not only reinforces the usefulness of the service but can also reduce churn caused by misunderstandings or unawareness.
10. Announcing new features and encouraging usage
Modern chatbots can serve not only as support tools but also as communication channels that promote product development and encourage feature adoption. A proactive approach involves using the chatbot to inform users about new in-app capabilities — turning passive updates into interactive micro-moments that drive engagement.
In one effective implementation, the chatbot regularly surfaces updates about new features, such as image recognition for detecting fraudulent transactions or submitting chargeback requests. What makes this mechanic especially effective is the use of interactive elements, such as buttons that let users test the new feature instantly. Instead of navigating through menus or update notes, users can explore new tools in real time, directly from the chat interface.
11. Promoting chatbot resolution before escalation
When users ask to speak with a human agent, it’s a key moment of truth in the service journey — and a well-crafted chatbot experience can turn it into an opportunity for positive intervention. Instead of immediately transferring the user, some chatbots are designed to gently reinforce their own ability to resolve the issue. The goal isn’t to block access to support, but to increase the user's confidence in the bot’s capabilities.
The best-performing solutions strike a careful balance: they promote the benefits of chatbot assistance — speed, availability, and accuracy — while making it easy to escalate when needed. This approach respects user autonomy, improves perception of the chatbot, and increases the chance of resolving the issue within the automated flow. As a result, banks benefit from higher containment rates and lower support costs, while users retain a sense of control and choice.
The findings from Chatbot Rank 2024 show that excellence in chatbot design is no longer about handling FAQs — it’s about delivering value at every step of the customer journey. The best implementations combine intelligent routing, predictive logic, and behavioral awareness to reduce friction and increase satisfaction. For product teams, these solutions offer not just inspiration, but a proven framework for improving digital support, lowering operational costs, and turning chatbot interactions into strategic business drivers.
