From chatbots to automation, artificial intelligence (AI) is reshaping enterprises and the way it interacts with its customers. According to a Gartner’s report, the global business value derived from AI is projected to total $1.2 trillion in 2018 and forecasted to reach $3.9 trillion by 2022. The report identifies customer experience as one of the primary sources of AI business value, followed by new revenue and cost reduction.
AI has been around for decades, but it’s in the fourth industrial revolution that AI is beginning to take root in almost every business application. IDC predicts that by 2019 “40% of digital transformation initiatives will be supported by cognitive/AI capabilities. The push for AI comes from how consumers are engaging with businesses on a daily basis.
While there are many uses for AI, there are three themes that we come across:
Use of AI for Data Insights
For every organization, gathering customer behavior datasets and making sense out of it is a challenge and also cumbersome. As customers expect omnichannel experience, AI and customer journey analytics are key components to deliver the experience. Most of the current AI use is restricted to the front-end or pre-purchase phases of the customer life-cycle. But AI can go beyond to cover the entire lifecycle, from product usage to account management and logistics.
High-performing banks are already using AI to assess their customers with pre-built predictive analytics and AI-enabled customer journey. For example, The Royal Bank of Canada and Israel Discount Bank have an AI platform called Personetics to help their customers manage their finances. The Self-Driving Finance Platform, promises a safe and effortless guide for consumers to manage their day-to-day finances through cognitive systems and a fully automated savings solution. With AI, the banks are able to track every customer interaction, spending patterns and find its relationship in a dataset – aiding prediction of future behaviors. These insights help in building an optimal CX by providing actionable insights.
Customer Service and AI
For most banks, customer service involves the interaction of employees with customers across various touch points. To meet the increasing demand for customer interaction, companies are looking at chatbots and virtual assistants to enhance their customer service. As per Juniper Research, “chatbots hold the potential one day to replace the task of many human workers with AI”. It forecasts that by 2022 chatbots would be responsible for cost savings of over $8 billion per year, up from $20 million in 2017. This trend is being observed in the banking sector as well with a prediction of 93% of successful messaging banking bot interactions in 2022.
Chatbots are helping organizations reduce costs of implementation, introduce an easy-to-use conversational interface to customers, take care of simple account creation/ KYC requirements and also be able to provide 24/7 digital support to its customers. For example, Wells Fargo& Company uses an AI-driven chatbot plugged to the Facebook Messenger platform. The chatbot responds to natural language messages and is accessible to its customers directly on social media, regardless of the device being used. Similarly, Bank of America is rolling out their AI-platform, Erica – an intelligent virtual banking assistant. The platform would be integrated into the mobile banking app to enhance customer engagement.
The potential of chatbots go beyond just a messaging system. Future chatbot technologies would include facial recognition to enable zero-click transactions, application of virtual reality for better data visualizations and using IoT-enable devices for voice interactions.
Mass Personalization with AI
Today’s digital customers demand hyper-personalization at every step of their journey with a brand. This level of personalization goes beyond personalized emails and suggested products lists. With predictive analytics and AI, banks are able to deliver individual customer preferences at scale, integrating with their daily lives.
For banks, personalization is a viable growth strategy, providing opportunities for up/cross-selling products and services. This would require the integration of multiple datasets to provide an overview of a customer’s credit history, spending habits, interests and life events. Personalizing the user experience is also delivered through hyper-customized content, improving click-through rates.
When it comes to personalization, mBank is seen as the leader in innovative banking solutions. The Poland based financial institution uses predictive analytics to identify customer behavior and use the information to initiate direct conversations and other marketing efforts. For added security some banks, such as Royal Bank of Scotland and NatWest, use biometrics for authentication systems in mobile banking.
In this digital era the financial sector is changing, and while customer service is a priority, security and regulatory compliance also need to be looked at. Banks have to strike the balance between the two and transition smoothly into adopting technologies. How can one achieve this balance? Read about it in our blog “Best Practices to watch out for when implementing AI in Customer Experience”.
