Technology
Conversational AI: What is it and why do the financial services need it?
Global Banking and Finance Review
Company
Conversational AI is transforming financial services by enhancing customer interactions and optimizing operations with technologies like NLP and virtual assistants.
By Charles Sutton, Financial Services and FinTech Lead, EMEA, NVIDIA
The Financial Services industry is sitting at a crossroads, squeezed by mounting financial pressure due to an increase in risk tied to financial volatility, a higher volume of customer service enquiries and the need to further develop digital engagement and channels with branches closing in today’s COVID-19 environment.
There has been a major shift towards digital engagement in the last seven years. Accelerating at a rapid pace, consumers are becoming more accustomed to using digital channels when dealing with all aspects of life. They’re using mobile banking apps, artificial intelligence (AI) infused virtual assistants to share immediate security alerts, and they’re even transferring money between accounts using just their voice.
In many cases, consumers today are interacting with AI and not even realizing it. It’s as simple as waking your home voice assistant with a “Hey” or using speech-to-text functions, both of which are built using elements of 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 using their own speech.
As a person speaks, the device works to understand and find the best answer 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 and several complex models that need to run in less than 300 milliseconds to deliver a great user experience.
Conversational AI is primarily based on three key processes:
Optimizing the Call Center with Conversational AI
Conversational AI can significantly impact the customer service experience.
Just a one-point decline in a business’ customer experience score can equal $124 million in lost revenue for multi-channel banks.
Customer service agents can deliver an improved customer experience with AI. Enabling agents with real-time insights can both reduce their workload and deliver a speedier interaction for customers. It can generate personalised, recommended offers and next-best actions for each customer based on their individual data. And it can transcribe calls, taking notes for the agent, reducing their post-call reporting time and allowing the agent to quickly and accurately support more customers.
AI can optimise insights for the service delivered, too. It can measure a customer’s sentiment from their experience, ensure consistent quality control across all customer interactions and provide context-aware analysis to ensure all issues are resolved.
Virtual Assistance with Conversational AI
With the increased amount of remote working today, organisations face increased workloads to process much larger volumes of calls.
Conversational AI can be personalised, customised to the user, and available day and night.
With a virtual assistant, customers can have a conversational, human-like dialogue with intelligent, instantaneous responses about all types of inquiries such as account-related issues, product applications, transaction inquiries, and claims.
Larger, more complex systems with access to a user’s information can understand, search and respond. They know the context of each user’s previous interactions, they can be nuanced and make cultural references, wordplay, adapt the tone of the conversation and even integrate with product recommendations.
A virtual assistant can also support customers with disabilities. For customers who are unable to interact with a keyboard or read a screen, conversational AI can provide a solution for every customer’s specific needs.
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.
Using Conversational AI to Reduce Fraud
Call centers can be a weak spot when it comes to fraud defense, with a reported over 80% of fraud going undetected.
Customer service agents are focused on just that – delivering the best service to the customer. Scrutiny or suspicion of a customer’s activity can risk lower customer satisfaction and lost revenue.
Conversational AI can be used to identify fraudulent account activity like identity theft by using sentiment and confidence analysis, pattern recognition and voice based identity authorization.
Through intent processing, NLP is also able to detect application fraud, whether exaggerated claims, illegitimate claims, or misrepresentations in applications to get lower premiums.
Conversational AI can Transform the Customer Experience
While conversational AI is still becoming more popular, a shift towards e-commerce and a digital-first customer experience means people are increasingly using AI in their day-to-day activities.
46 percent of people use AI every single day, 23 percent of customer service organizations are using AI-powered chatbots and 62 percent of consumers say they’re open to using AI to improve their daily experiences.
By reducing fraud, delivering smoother, faster customer interactions through a virtual assistant or better enabled agents, conversational AI will play a key role in delivering a better customer service experience.
And the demand for conversational AI is only going to keep growing, with the market estimated to reach a value of $16 billion by 2024.
The article discusses the importance of Conversational AI in financial services, highlighting its impact on customer service and operational efficiency.
Conversational AI improves customer service by providing real-time insights, reducing workload, and offering personalized interactions.
Key technologies include Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS).
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