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Satisfy the strictest security requirements

Rasa works within the IT landscape of large banks. Fully control your infrastructure and data privacy.

Advanced NLU and dialogue management

Customize and train language models specifically for banking and financial services terms. Manage multi-turn dialogues with machine learning.

Localized Support

Language-agnostic NLU allows you to support customers in multiple regional markets with AI assistants in any language.

Because banking doesn’t stop when business hours end.

Today’s financial services customers manage their money on the go—any time and any place. Staying connected and having instant access to accounts isn’t just a nice-to-have, it’s a necessity.

Increasingly, just having access to accounts isn’t enough. Banking customers want to navigate support resources without waiting in a phone or chat queue, and they don’t want to hunt through complicated portals to find the right link. The best banking customer service solutions are proactive, they’re intuitive, and they’re conversational.

Human-staffed call centers are part of the equation, and an important one. But even with round-the-clock staffing, customers with simple transactions still wind up waiting in line to speak with an agent. Excelling at banking customer experience—at scale—requires smarter automation in addition to traditional strategies.

Rasa offers banks and financial institutions a way to serve more customers to the highest standard. Rasa’s conversational AI platform powers virtual assistants in some of today’s largest banks, allowing customers to get the help they need and freeing human agents to focus on solving the really tough challenges.

What makes Rasa different?

Rasa deploys on-premises, allowing banks to protect their customers’ most sensitive data while protecting their own IP. Rasa is powered by cutting edge NLU and machine learning for dialogue, allowing the most natural, automated conversations. And finally, Rasa’s open source flexibility allows teams to integrate with any system, for virtual assistants that can execute complex transactions for customers.

Give your customers a bank teller in the palm of their hand.

  • Transfer funds
  • Open and close accounts
  • Update contact details
  • Request replacement credit cards

Get our exclusive content about conversational AI in banking

We'll send you premium material from banking industry leaders such as National Bank Canada and Moneylion. Learn how banking professionals are using conversational AI to improve customer experience using AI-enabled services, build robust, multi-domain dialog systems, and hear our CEO and Co-founder Alex Weidauer's thoughts on the future of banking AI assistants.

Case Study

Automating 30% of simple customer service requests, to focus on the really important tasks

N26

The Challenge

N26, the leading mobile bank in Europe with over $500M in funding, has seen tremendous growth to over two million customers in just a few years. The company operates across many different national markets in Europe, offering customer service in five languages, and plans to expand to the US.

N26 is facing customer service challenges while keeping up with strong growth. To improve customer experience through faster responses, N26 decided to investigate utilizing AI in their customer service operations.

N26 found existing cloud-based solutions weren’t able to fulfill its customization as well as data protection needs. In addition, the company wanted to automate more complex, back-and-forth conversations.

The Solution

Using Rasa, N26 was able to get from idea to production within four weeks and deployed the bot on their secure cloud environment with full data control. A product team consisting of data scientists, designers, developers, and product managers, worked closely with customer service to identify the major use cases. Specifically, the team was able to handle more complex conversations using Rasa’s machine learning-based dialogue AI instead of hand-crafting each rule. Now the AI assistant is running in five different languages in their mobile and web app, even handling complex tasks such as reports of credit cards being lost or stolen.

The Results

N26’s team was able to tweak the machine learning models to peak performance with their own data sets. Setting their AI assistant live in the mobile app quickly resulted in automated handling of 20% of customer service requests. N26 is working on bringing this to 30% and beyond.