Banking Intelligence in Action
By Team ZineOne June 7, 2019
Top 5 Examples of AI & ML to Enhance Your Bank’s Customer Experience Strategy
Experts often discuss the power of Artificial Intelligence (AI) and Machine Learning (ML) models that intelligently react to customer intent in real time, but what do these capabilities look like in action? In our “Intelligence in Action” series, we’re exploring the most robust real-world applications of AI and ML technology currently enhancing the customer experience strategy across industries. This week, we begin with banking.
For the banking industry, increased intelligence leads to reduced expenses—in fact, it’s estimated that AI could save the banking industry $1 trillion by 2030. The world’s top financial institutions are taking note, rapidly investing in intelligent technology solutions with the power to address customers’ most pressing needs while also impacting their bank’s bottom line. Here are the top examples of this banking intelligence in action:
1 | Wells Fargo
Their Solution: Wells Fargo is piloting an AI-powered chatbot through Facebook Messenger, which allows customers to interact with a virtual banking assistant directly on the social media platform.
How it Enhances Their Customer Experience Strategy: The AI chatbot service dynamically addresses customers’ everyday needs, from resetting passwords to accessing up-to-date account information, viewing recent transactions, and even using their location to find the nearest ATM. Its deployment via Facebook Messenger makes the pilot bot service a cross-channel solution—ready for use on desktops, laptops, and smartphones—offering real-time convenience to banking customers. Wells Fargo has been using Facebook as a platform to help customers since 2009, and this pilot is an intelligent extension of that effort.
2 | Capital One
Their Solution: Capital One built its own intelligent customer assistant, called Eno, with custom Natural Language Processing (NLP) technology.
How it Enhances Their Customer Experience Strategy: For Capital One, all AI and ML solutions must fall in line with what the company calls its “humanity- and responsibility-centered philosophy.” That’s why Eno was built with the human touch of NLP. The virtual assistant holds text message conversations with customers, interpreting and responding to their account questions (such as “What’s my balance?” or “When is my payment due?”), even if they include typos or emojis. By giving customers the information they need in real time, in context, and in the confines of a friendly text message, Capital One’s Eno uses AI to simplify and humanize banking interactions.
3 | Bank of America
Their Solution: Bank of America launched Erica, its own AI-powered virtual assistant with machine learning capabilities, on its mobile app.
How it Enhances Their Customer Experience Strategy: Available only on Bank of America’s app, Erica is a solution built for today’s increasingly mobile approach to banking. Like other virtual financial assistants from Wells Fargo and Capital One, Erica can answer customers’ questions about their accounts in real-time and on-the-go; however, it also uses machine learning models to understand and cater to customers’ long-term financial habits and goals, becoming increasingly effective over time. This intelligent capability makes Erica, and other virtual assistants like it, a solution that customers can rely on for predictive assistance.
4 | JPMorgan Chase
Their Solution: JPMorgan built an AI-powered platform for Contract Intelligence (COIN) that streamlines the analysis and review of financial documents.
How it Enhances Their Customer Experience Strategy: The COIN platform uses AI and ML models to save JPMorgan employees 360,000 hours annually, rapidly reviewing the data on loan documentation and other financial forms. Not only does this allow banking customers to receive feedback on their documentation more quickly, but it also greatly reduces loan-servicing mistakes by eliminating the potential for human error. By efficiently automating form filing and document analysis processes from systems of record on the back end, JPMorgan’s COIN platform makes banking easier for employees and customers alike.
5 | Sberbank
Their Solution: Sberbank, the largest financial institution in Russia, created an ML algorithm called AutoML that is capable of forming new models to meet changing financial needs.
How it Enhances Their Customer Experience Strategy: AutoML is an algorithm system that can rapidly formulate new ML application solutions as needed. Today, AutoML is being used by Sberbank for a variety of applications, including predicting the potential creditworthiness of customers who apply for loans. AutoML is also being used to better target sales campaigns using next-best action marketing. As solutions like AutoML become increasingly common in the financial sector, banks will be better equipped to provide predictive, hyper-personalized customer experiences.
Ready to discuss how intelligent technology can enhance your bank’s customer experience strategy? Contact ZineOne to learn more about how our AI-powered Customer Engagement Hub (CEH) promotes better banking by intelligently addressing in-the-moment customer needs.
Request a Demo
We look forward to getting to know your business!