Protect customers’ financial data with AI assistants that don’t sacrifice trust for convenience

Financial services companies around the world use Rasa to build AI chatbots that meet the strictest data security standards, on the infrastructure of their choice.

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  • Lemonade
  • N26
  • Adobe
  • Orange

Customers expect banks to do business beyond 9-5

Be there when and where your customers are, with embedded AI chatbots on mobile or web. Machine learning powered dialogue management allows your conversational AI bot to handle complex customer needs outside of normal business hours.

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

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.

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.

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