“In August 2020, This is Money found that – in some cases – it took three hours for customers to get through to a human when contacting their bank. Of course, this was in the middle of a global pandemic, so you can excuse a certain amount of disruption to services, but this is where conversational AI is a boon.
How can banks use it to improve customer service?
Conversational AI can lead to faster resolution times
Using conversational AI can result in a better resolution for the customer
Conversational AI can help generate personalised recommendations”
“Chatbots play an increasingly important role in customer service, support and sales. Pickell reflected that 95% of customer interactions are expected to take place via an AI chatbot or live chat by 2025. Additionally, a Gartner report indicated that by 2022, 70% of white-collar employees will interact daily with conversational platforms.”
“For financial institutions that have yet to adopt conversational banking, the potential user base already exists. Nearly two-in-five U.S. adults are now users of smart speakers, such as Amazon Alexa or Google Home.
In November 2020, eMarketer estimated that 128.0 million people in the US used a voice assistant – 44.2% of internet users and 38.5% of the total population.
The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, according to Autonomous Next research. Notably, the front office (conversational banking) and middle office savings account for $416 billion of that total.”
“The internet giant has over the years sunk billions of dollars into areas from language learning to voice interaction and autonomous driving, betting on smart devices and vehicles of the future. Now, aided by years of investment and Beijing’s bid to build smart nationwide infrastructure, these efforts are finally paying off.
Sales last quarter rose 4.8%, the fastest pace in 2020, fueled by a 52% increase in its non-advertising businesses like AI cloud.”
“While I’ve been using Excel’s mathematical tools for years, I didn’t come to appreciate its use for learning and applying data science and machine learning until I picked up Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods by Hong Zhou.
There’s a chapter that delves into the meticulous creation of deep learning models. First, you’ll create a single layer artificial neural network with less than a dozen parameters. Then you’ll expand on the concept to create a deep learning model with hidden layers.
In the last chapter, you’ll create a rudimentary natural language processing (NLP) application, using Excel to create a sentiment analysis machine learning model. You’ll use formulas to create a “bag of words” model, preprocess and tokenize hotel reviews and classify them based on the density of positive and negative keywords.”