Charles Sutton, Financial Services and FinTech Lead EMEA, NVIDIA
Over the last seven years, there has been a significant shift towards digital engagement. Its growth shows no signs of slowing, with consumers becoming more accustomed to using digital channels for all aspects of life. They’re using mobile banking apps, artificial intelligence (AI) infused virtual assistants to get real-time security alerts, and they’re even moving money between accounts using just their voice.
And in many cases, consumers are interacting with AI without even realizing it. From waking a home voice assistant with a simple “Hey” to using speech-to-text functions for hands-free typing, both are built using Conversational AI.
What is Conversational AI?
Conversational AI is the application of machine learning to allow humans to interact naturally with devices, machines, and computers by simply speaking to them. As a person speaks, the device works to understand and find the best answer, providing a response with its own natural-sounding speech.
It may sound simple, but the technology behind conversational AI is complex. It involves a multi-step process that requires a massive amount of computing power. Delivering a seamless user experience requires several complex models that need to run in less than 300 milliseconds.
Conversational AI is primarily based on three key processes:
- Automatic Speech Recognition (ASR), which takes spoken words and converts them into readable text.
- Natural Language Processing (NLP), which reads written text, understands the context and intent and then generates an intelligent text response.
- Text-to-Speech (TTS), which converts the NLP text response to natural-sounding speech, with human-like intonation and clearly articulated words.
Transforming the Customer Experience with Conversational AI
The Financial Services Industry is under pressure, with rising levels of risk, higher volumes of customer service enquiries, and the need to develop digital channels to balance the closing of branches, especially in a post-COVID-19 environment. Just a one-point decline in a business’ customer experience score can equal $124 million in lost revenue for multi-channel banks.
Conversational AI can significantly improve the customer service experience throughout the customer journey. AI can enable customer service agents to deliver an improved customer experience, providing them with real-time insights to reduce their workload and deliver a speedier interaction for customers. It can generate personalized, recommended offers and next-best actions for each customer based on their individual data. It can even transcribe calls and take notes for the agent, reducing their post-call reporting time and allowing the agent to quickly and accurately support more customers.
With growing volumes of customer calls, a virtual AI assistant can be available day and night to assist with simple enquiries such as account-related questions or product applications. Customers can have conversational, human-like dialogue with intelligent, instantaneous responses, customized for the user it’s talking to. AI virtual assistants can also support customers with disabilities who might not be able to interact with the keyboard or screen.
UK-based NatWest’s digital assistant, Cora, is handling 58% more inquiries year on year, completing 40% of those interactions without human intervention. According to Juniper Research, 90% of customer interactions will be automated by 2022, saving banks $7 billion by 2023.
Agents should be focused on delivering the best customer experience, which means that fraud can go undetected at the call center. In fact, there’s a reported 80% of fraud going undetected today. As a call takes place, conversational AI can spot fraudulent activity like identity theft by using sentiment and confidence analysis, pattern recognition and voice-based identity authorization.
Conversational AI for Document Extraction and Risk Monitoring
Financial applications/market monitoring pulls unstructured data from many sources such as the news, customer applications, events, documents, proprietary data, market moves or filings. To collate such a large amount of varied data, businesses can use NLP to extract data from documents, regardless of language or layout. It can perform text analytics, entity and event extraction, and relevance and sentiment analysis to extract the most important information for decision making.
This type of AI document analysis can detect early warning signs of risk, like defaults, bankruptcies, lawsuits, or fraud. It can also improve lending decisions, be used for investment risk management and accelerate due diligence for Anti-Money Laundering (AML) and Know Your Customer (KYC) compliance.
By making this monitoring automatic, risk mitigation can minimize costs, and businesses can target investment opportunities with alpha returns and can gain operational efficiencies by customizing NLP for specific use cases. Banks and insurers can also use document processing to process all types of applications across unstructured document types, speeding up document turnaround time, reducing error rates and significantly improving document processing costs.
Accelerating Business Performance with Conversational AI
While AI continues to become more mainstream, there’s a shift towards e-commerce and a digital-first customer experience, where people are using AI in their day-to-day activities — in fact 46 percent of people are using it every single day.
Throughout the customer experience, conversational AI can deliver a smoother, faster experience, it can be on-hand to help all day, every day, enable agents to do their best work and reduce fraud — all at the same time.
