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Conversational AI in Banking: Business Intelligence analysis for Service Providers
Conversational AI in Banking: Business Intelligence analysis for Service Providers
Ganesh Subramaniam

Senior Consultant at Draup

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Conversational AI in Banking: Business Intelligence analysis for Service Providers

10 Jan 2020

As today’s tech savvy customer is rapidly moving towards online banking, banks and financial services firms are investing heavily into providing digital services to compliment the personal in-person services. Chatbots were the early favorites for the financial institutes as the face of the applications to interact with the customers.

However, the sharp increase in the number of people using voice enabled devices (driven by Siri, Alexa, Google, Samsung)  has made banks realize the importance and opportunities of implementing voice assistants. This replaces much of the work of customer-care call centers and drives down costs as well as speeds up customer care calls.

 

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Banks use voice to offer new services such as managing loan payments, tracking account statements and participating in workshops. Major use cases include:

  • Digital Banking services: Customers can easily access complex wealth management functions like investment research, investment recommendations, financial planning and advisory, account opening, order placement, and trade status using voice assistants.
    • Capital One launched a service using Amazon’s Alexa with services including credit card payments, account balances and getting a snapshot of recent spend.
  • Improved customer service: NLP and predictive analytics supported voice assistants help in rapidly streamlining call center operations and improve customer satisfaction scores by 20%.
    • Bank of America’s voice assistant Erica helps customers access account balance, transfer money between accounts, view statements, and schedule bill payments.

 

To use voice assistants, banks need to improve speech transcription capabilities. NLP is used to classify call types, structure data and develop sentiment measures. Predictive analytics allows bank to anticipate future customer behavior based on the call center interaction. This helps to develop strategies to improve customer outcomes. Service providers who can build NLP and predictive analytics based solutions are required by financial institutions for developing voice assistants.

  • Infosys’s NIA Chatbot platform is architected to leverage the best of the breed technologies to create conversational channel. This platform provides customized integration with leading commercial speech recognition software like Nuance and NICE.
  • TechM partnered with Avaamo to build conversational AI platform solutions, with a specific focus on machine learning. The solution focuses on creating a seamless experience across their enterprise channels for customers.

 

Banks are looking to partner with services providers having solutions based on AI, ML, NLP and predictive analytics for implementing voice assistants. Service providers who enable voice assistants’ technology to BFSI should provide the following key offerings:

 

  • Identify appropriate conversational AI use cases as well as the right technology and channels
  • Design, develop, test and deploy bots for various channels integrated with VPAs, Smart Speakers and Messaging Platforms like Alexa, Siri, Cortana.
  • Utilize multi-media, vision/gestures, virtual avatars and context-awareness to enrich conversational interfaces.
  • Extract insights from conversations, ensuring alignment with business objectives.
  • Leverages AI-centric approaches to synthesize knowledge from enterprise knowledge assets.