The momentum behind chatbots is growing. American Banker reports that major banks are actively experimenting with chatbots. Chatbots is short for chat robot, a computer program that simulates human conversation, or chat, through artificial intelligence. Typically, a chatbot will communicate with a real person, but applications are being developed in which two chatbots can communicate with each other. Chatbots are used in applications such as e-commerce customer service and call centers.
Banks trying to decide how to use chatbots include Ally, BBVA, Bank of America, Barclays, Capital One, Societe General/a>, and USAA. A survey conducted by Personetics found that 87% of bankers say they plan to do something with chatbots by 2020.
Penny Crosman, the author reports that a growing number of vendors are offering prefab chatbots trained with documents, data, and conversations about financial products and topics. Some are intended to replicate the interaction with a human, while others are styled more like workhorses. The author describes four types of chatbots being used today.
Types of chatbots
The conversationalist – Kai is the Chatbot from Kasisto. Kasisto is a spinoff of the research lab, SRI International. SRI developed the first public Artificial Intelligence (AI) Siri, which Apple purchased in 2010 for use in the iPhone.
SearchCIO defines AI as the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction. Particular applications of AI include expert systems, speech recognition, and machine vision.
“Siri knows a lot — it’s very broad but very shallow,” Zor Gorelov, CEO and co-founder of Kasisto. A group of SRI researchers realized a financial services chatbot would have to be narrow and deep and started working on one. BBVA approached the group in 2009. BBVA was looking for human-like interactions from a chatbot, and the two organizations partnered to create a virtual banking assistant. Mr. Gorelov said the SRI researchers “interviewed people across the banking universe at BBVA … transcribed tens of thousands of calls.”
A staff of full-time writers called “artificial intelligence interaction designers” produces dialogues for Kai. American Banker says they also constantly monitor the behavior and user interactions. General banking knowledge has also been embedded. “You can ask Kai questions about CDs, IRAs, credit scores — it’s the smartest banker you can imagine from a customer onboarding point of view: how do I open an account, what document do I need?” Kasisto’s Gorelov says there are 10 to 11 messages exchanged between Kai and users during an average session,
The article noted that Kai is used by digibank, a mobile-only bank launched in India by DBS Bank in Singapore, and it’s being piloted by the Royal Bank of Canada.
The Doer – Personetics has built a library of customer insights for its chatbots. Its builders fed the chatbot technology financial services information for five years and fueled it with unsupervised and supervised machine learning, natural language understanding, logic inference, and associative knowledge according to AB.
While other chatbots might aim to simulate a real conversation, Personetics tries to make it clear the customer is not dealing with a human to avoid potential confusion.
Eran Livneh, vice president of Personetics explained the chatbot is akin to an employee who understands banking and serving customers really well. The chatbots can walk customers through steps, provide predictive messages and behavior insights, and automatically perform tasks like money management.
So though they can “chat” with customers, they’re primarily designed to actually do things for them. Ally Bank and Societe Generale use chatbots by Personetics.
The linguist – Montreal-based software company North Side, specializes in giving its chatbot, VerbalAccess, a precise understanding of language, whether spoken or typed, through natural language processing technology.
The article notes that North Side didn’t start out in banking. Originally it created a video game that lets players communicate with characters. “That’s how we made our natural language understanding pipeline robust,” said Eugene Joseph, North Side’s CEO.
North Side doesn’t try to glean insights from analyzing customer behavior. Instead, it takes commands and acts on them, such as making a payment or displaying a transaction. If a user asks, “What have I spent on coffee in the last month?” North Side’s chatbot will understand the question and translate it to an API call that will extract the answer automatically. It would make the same calls that might be made by a mobile banking app or online banking site, but with the added ability to translate from the imprecise way a human might ask something to language the software can understand..
The chatbot is trained to clarify users’ questions. “If what is said is incomplete, it will elicit the missing information,” Mr.Joseph said. “That’s very important because people speak in an incomplete way. We know what to ask for.”
The Teller – Sidharth Garg began working on his chatbot, Teller, while at Columbia Business School. He told AB, “I wanted to use the recent advances in natural language processing and the opening of messaging platforms to help people learn the basics of personal finance.”
To feed Teller the information to answer customers’ questions, he went through blogs and personal finance books, attempting to answer every general personal finance question he could think of. Mr. Garg explained the goal was to make retirement options easy to understand. He “…put together Buzzfeed-style graphics that explain the different types of retirement accounts into something someone could understand on their mobile device.”
He decided to focus on the business-to-consumer market. “People are more inclined to chat with a banking assistant that comes from an institution they already trust,” Mr. Garg told the author. “They already have a captive audience of customers and they would also allow for easy integration with their bank accounts.”
Recently, he’s been running a pilot program with Brooklyn Cooperative Federal Credit Union that has provided more real-world data to train the chatbot. For now, the bot is answering only the most general questions for the credit union’s customers. Eventually, the plan is to integrate the chatbot with the credit union’s back-end system, to let it answer questions specific to customers’ accounts.
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I have written about chatbots a couple of times on the Bach Seat. Google, Facebook, Microsoft, and even Pizza Hut are experimenting with bot-to-human interactions. Chatbots will get smarter as more people keep using them, and as developers perfect the tools to turn the software into what people want and need. Like Personetics’ Livneh said, “Customers generally like things that help them with the day-to-day activities.” Chatbots will also cost jobs.
Ralph Bach has been in IT long enough to know better and has blogged from his Bach Seat about IT, careers, and anything else that catches his attention since 2005. You can follow him on LinkedIn, Facebook, and Twitter. Email the Bach Seat here.
William English, who helped build the first computer mouse, has died at the age of 91. Mr. English built the first mouse in 1963, in collaboration with his colleague Doug Engelbart while they were working on at the Stanford Research Institute (now SRI International).#/media/File:SRI_Computer_Mouse.jpg "First mouse")
The first version of the mouse was contained in a wood case. The mouse consisted of two potentiometers – rolling wheels at 90-degree angles that would interpret the wheels’ X and Y coordinates – vertical and horizontal positions – of the wheels as they moved across a desktop. Prior to the development of the mouse laborious and error-prone keypunch cards or manually set electronic switches were necessary to control computers. “We were working on text editing – the goal was a device that would be able to select characters and words,” Mr. English told the Computer History Museum in 1999.
uring 1968, in what some have described as “the mother of all demos” the mouse made its public debut. The mouse was a part of a demo by Mr. Engelbart, at a computer conference in San Francisco. He used SRI’s connection to the Advanced Research Projects Agency Network (ARPANET), the primary precursor to the Internet to show off a working real-time collaborative computer system known as NLS (oN-Line System). Using NLS, the colleagues publicly demonstrated many of the technologies we take for granted today – video conferencing, multi-person document collaboration, screen-sharing and an early form of hypertext.