If you manage technology for a consumer bank you’ve probably invested time and energy researching conversational banking solutions. You’ve probably discovered a host of simple chatbot solutions that provide scripted responses to a pre-determined set of highly predictable questions. With these chatbots, deviation from a roster of standard FAQs means human intervention is required to complete an inquiry or task. And this has likely led you to ask a very logical question: What good is a chatbot solution if it doesn’t dramatically limit the extent of human labor required to better serve your customers?

Fortunately, voice and text-based conversational Artificial Intelligence (AI) solutions driven by cognitive intelligence enable banks to provide personalized, informed interactions. Simple chatbots are satisfactory for automating the most basic tasks, such as inquiries on ATM locations, account balances and payments. However, consider how often your customers’ questions diverge from strict idiomatic structures with very specific, personal opinion-based questions. Why not implement a solution that can handle whatever scenario a banking customer presents?

Going Beyond Scripted Responses

For example: Customers who ask questions such as, “Should I apply for a small business loan?” or “Should I cash out my 401K?” aren’t looking for a generic scripted answer. With a cognitive system in place, your bank can leverage machine learning, dialogue variance and historical memory to provide informed opinions about customers’ questions and concerns. The system can research consumers’ banking history, access market data, perform calculations and, most importantly, inquire about their financial goals in order to render educated recommendations.

Another critical feature most chatbots lack is the ability to switch conversational context. If a customer calls to consolidate several loans, and during the conversation he remembers that he wants to move some of his checking balance into his savings account, a chatbot would need to handle these requests in two separate conversations – requiring re-verification, a restating of intent and superfluous dialogue volleys that will only frustrate the customer. True cognitive intelligence can execute the checking-savings transfer and return to the loan consolidation conversation without needing to restart the entire process from the beginning.

Understanding Customer Intent

Customers do not speak in precise banking terminology, which makes conversational AI even more valuable in end-user interactions. If a customer says, “Can I lump my loans together?” or “I have too many loans and I hate paying them off separately is there any way I can make one single payment?” a chatbot would be unable to discern that the customer wants to consolidate loans. A conversational banking solution can use its customer knowledge (he makes loan payments on time every month) and sophisticated dialogue comprehension (“lump loans together” means “consolidate”) to at the very least ask the customer, “Are you referring to student loan consolidation?” Conversational AI – which can be delivered through either a text- or voice-based interface – can distill customers’ words and intents for actionable results.

The conversational banking era is here. Banks are investing in cognitive technologies that will give their institutions a literal voice that can interact with consumers. Entrepreneurs are launching conversational banking start-ups. While starting with an initial set of use cases is important, it’s critical that you think long-term. Wouldn’t it be ideal to look beyond automating balance checks and build a conversational banking experience that will be relevant and market leading five years from now?

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