Emerging Technologies Operational Excellence Transformations

The Future of Work – Virtual Assistants Enriching Human Work

Many of us use virtual assistants like Siri, Cortana, and Alexa. Cognitive technologies that power these solutions help do things that were once thought only people could do. With increased digitization, we have more data than we can fathom. Solutions built on cognitive technologies can convert vast and complex data into actionable contextual insights in real-time. Virtual Assistants (VAs), with their digital brain, can transform banking services, customer engagement and experience, and operational performance. The application of VAs in banking is growing exponentially. 

For example, banks can use

  • Chatbots to provide answers to customer queries, provision services, etc. Many organizations are already using them internally (e.g., HR, IT, Accounts) as well in external customer-facing channels.
  • VAs are used to help customer service agents with relevant insights in real-time. Such insights can be knowledge articles, contextual and real-time insights to address customer requests, or even propose cross-sell / up-sell opportunities.
  • VAs powered by conversational analytics (including emotional elements) are used to guide and nurture agents/advisors.
  • Some organizations use them in the background for monitoring business processes (e.g., fraud detection, AML) and alert staff when required.

VAs enrich the work of employees. For example, in Wealth Management it is believed that the high-net-worth clients need human advisors. While it is true, technologies like VAs can help deliver highly personalized experiences. They free up the bandwidth of human advisors to focus on relationship building and to achieve new levels of expertise and responsiveness.

Data is the foundation for building VAs. Artificial Intelligence (AI) techniques like Machine Learning, Deep Learning, Natural Language Processing/Generation, etc. are used to transform data into insights. For successful implementation, one should ensure that there is no bias – intentional or unintentional. And they are trustworthy. Building transparency and bringing them under the model risk management umbrella is another critical aspect.