Banks are increasingly turning to AI to improve customer experience and drive revenue growth. AI helps them acquire, serve, and retain customers.
Forrester Research has highlighted some of the major applications of AI in banking customer service and their implementations in some of the popular banks across the world.
Banks can use AI to develop more accurate customer segmentations. Ulster Bank in the Republic of Ireland and Northern Ireland is using machine learning to score and prioritize customers, leads, and opportunities. The technology, which is powered by Salesforce Einstein and Atos, also allows the machine to learn from each interaction.
AI can also create dynamic content for target segments. Marketing team uses this content for targeting and personalization, to enhance velocity, granularity, and efficacy, Forrester Research said.
Of late, AI applications based on semantics and natural language generation are gaining traction among major banks. Citi in the U.S., for example, is using Persado’s semantics-based platform to communicate with different customers more effectively through a specific selection of words. As a result, the bank has noticed that its credit card email open rates are up 70 percent and click-to-open rates are up 114 percent.
Many banks today lack insight into the effectiveness of marketing campaigns across multiple channels and product offers. AI technologies can be used to orchestrate and execute cross-channel marketing campaigns. Zeta Global, an AI-driven marketing platform, has helped a large retail bank in the U.S to create channel attribution based on the recency and depth of engagement via email, direct mail, and contact center.
AI also helps prospects identify and research financial products. In one such implementation, OCBC Bank in Singapore has turned to Emma chatbot to help answer pre-sales FAQs and generate leads. According to the company, Emma facilitated S$33 million (US$24 million) in home loans in six months.
A few banks have turned to AI to help prospects open accounts easily. For example, Spain’s BBVA uses biometrics, facial recognition, and AI-infused optical character recognition (OCR) to let prospects open account with just a photo ID, a selfie and a video call.
ING Direct in Italy is using natural language technique from Expert System to enhance search in website.
National Bank of Australia (NAB) in Australia has turned to natural language processing, machine learning and natural language generation to help customers check account balance and transaction history on Alexa. Westpac in Australia has also implemented this technology.
Royal Bank of Canada (RBC) has enhanced its mobile banking app via NOMI Insights and NOMI Find & Save powered by Personetics. Customers can use this feature to identify their cash flow and make automatic savings.
Using natural language processing, Royal Bank of Scotland (RBS) has developed a customer service chatbot called Luvo that answers customer queries and help them perform simple banking tasks like money transfers.
Standard Chartered Bank in Singapore has partnered with Moneythor to develop customized solutions to analyze texts from the transactions. These solutions are powered by machine learning and text analytics.
Nina is the AI chatbot from Swedbank, which uses machine learning to assist customer service. In addition to answering normal customer queries, Nina passes on more complex calls to human agents. Since 2016, the bot has managed to achieve a 78 percent first-contact resolution rate within the first three months, in addition to a customer adoption rate of 30,000 conversations per month during this period.
USAA has implemented an in-app digital agent that uses conversational service solutions including natural language processing, machine learning and natural language generation to answer questions and guide customers to the relevant content or feature.
Source: Forrester Research