For starters, he was the former president of PayPal. And he once founded a mobile media monetization firm. And he also founded a company that facilitated mobile phone payments. And then he helped Facebook acquire Braintree, which invented Venmo. And then he invented Messenger’s P2P payment platform. And then he was appointed to the board of directors at Coinbase.
Human touch. Chatbots, providing an interface similar to human-to-human interaction, are more intuitive and so less difficult to use than a standard banking mobile application. They doesn't require any additional software installation and are more adaptive as able to be personalized during the exploitation by the means of machine learning. Chatbots are instant and so much faster that phone calls, shown to be considered as tedious in some studies. Then they satisfy both speed and personalization requirement while interacting with a bank.
“It’s hard to balance that urge to just dogpile the latest thing when you’re feeling like there’s a land grab or gold rush about to happen all around you and that you might get left behind. But in the end quality wins out. Everyone will be better off if there’s laser focus on building great bot products that are meaningfully differentiated.” — Ryan Block, Cofounder of Begin.com
Using chatbot builder platforms. You can create a chatbot with the help of services providing all the necessary features and integrations. It can be a good choice for an in-house chatbot serving your team. This option is associated with some disadvantages, including the limited configuration and the dependence on the service. Some popular platforms for building chatbots are:

Like apps and websites, bots have a UI, but it is made up of dialogs, rather than screens. Dialogs help preserve your place within a conversation, prompt users when needed, and execute input validation. They are useful for managing multi-turn conversations and simple "forms-based" collections of information to accomplish activities such as booking a flight.

Dan uses an example of a text to speech bot that a user might operate within a car to turn windscreen wipers on and off, and lights on and off. The users’ natural language query is processed by the conversation service to work out the intent and the entity, and then using the context, replies through the dialog in a way that the user can understand.
Haptik is one of the world's largest Conversational AI platforms reaching over 30 million devices monthly. The company has been at the forefront of the paradigm shift from apps to chatbots, having built a robust set of technology and tools that enable any type of conversational application. Our platform processed over a billion interactions to date and helps enterprises leverage the power of AI to automate critical business processes like Concierge, Customer Support, Lead Generation and E-commerce.
As people research, they want the information they need as quickly as possible and are increasingly turning to voice search as the technology advances. Email inboxes have become more and more cluttered, so buyers have moved to social media to follow the brands they really care about. Ultimately, they now have the control — the ability to opt out, block, and unfollow any brand that betrays their trust.
Not integrated. This goes hand-in-hand with the contextual knowledge, but chatbots often suffer from “death by data silo” where their access to data is limited. If a chatbot is “chatting with” a customer, they not only need to access the contextual data of their customer but also have access to every place where the answer to the customer’s question may reside. Product documentation site, customer community, different websites are all places where that answer can be.
Training a chatbot happens at much faster and larger scale than you teach a human. Humans Customer Service Representatives are given manuals and have them read it and understand. While the Customer Support Chatbot is fed with thousands of conversation logs and from those logs, the chatbot is able to understand what type of question requires what type of answers.
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The process of building a chatbot can be divided into two main tasks: understanding the user's intent and producing the correct answer. The first task involves understanding the user input. In order to properly understand a user input in a free text form, a Natural Language Processing Engine can be used.[36] The second task may involve different approaches depending on the type of the response that the chatbot will generate.
Other companies explore ways they can use chatbots internally, for example for Customer Support, Human Resources, or even in Internet-of-Things (IoT) projects. Overstock, for one, has reportedly launched a chatbot named Mila to automate certain simple yet time-consuming processes when requesting for a sick leave.[24] Other large companies such as Lloyds Banking Group, Royal Bank of Scotland, Renault and Citroën are now using automated online assistants instead of call centres with humans to provide a first point of contact. A SaaS chatbot business ecosystem has been steadily growing since the F8 Conference when Zuckerberg unveiled that Messenger would allow chatbots into the app.[25]

WeChat was created by Chinese holding company Tencent three years ago. The product was created by a special projects team within Tencent (who also owns the dominant desktop messaging software in China, QQ) under the mandate of creating a completely new mobile-first messaging experience for the Chinese market. In three short years, WeChat has exploded in popularity and has become the dominant mobile messaging platform in China, with approximately 700M monthly active users (MAUs).
Simple chatbots, or bots, are easy to build. In fact, many coders have automated bot-building processes and templates. The majority of these processes follow simple code formulas that the designer plans, and the bots provide the responses coded into it—and only those responses. Simplistic bots (built in five minutes or less) typically respond to one or two very specific commands.

Customer service departments in all industries are increasing their use of chatbots, and we will see usage rise even higher in the next year as companies continue to pilot or launch their own versions of the rule-based digital assistant. What are chatbots? Forrester defines them as autonomous applications that help users complete tasks through conversation.   […]
2010 SIRI: Though Siri is considered colloquially to be a virtual assistant rather than a conversational bot, it was built off the same technologies and paved the way for all later AI bots and PAs. Siri is an intelligent personal assistant with a natural language UI to respond to questions and perform web-based service requests. Siri was part of apples IOS.
"From Russia With Love" (PDF). Retrieved 2007-12-09. Psychologist and Scientific American: Mind contributing editor Robert Epstein reports how he was initially fooled by a chatterbot posing as an attractive girl in a personal ad he answered on a dating website. In the ad, the girl portrayed herself as being in Southern California and then soon revealed, in poor English, that she was actually in Russia. He became suspicious after a couple of months of email exchanges, sent her an email test of gibberish, and she still replied in general terms. The dating website is not named. Scientific American: Mind, October–November 2007, page 16–17, "From Russia With Love: How I got fooled (and somewhat humiliated) by a computer". Also available online.
This means our questions must fit with the programming they have been given.  Using our weather bot as an example once more, the question ‘Will it rain tomorrow’ could be answered easily. However if the programming is not there, the question ‘Will I need a brolly tomorrow’ may cause the chatbot to respond with a ‘I am sorry, I didn’t understand the question’ type response.
Once you’ve determined these factors, you can develop the front-end web app or microservice. You might decide to integrate a chatbot into a customer support website where a customer clicks on an icon that immediately triggers a chatbot conversation. You could also integrate a chatbot into another communication channel, whether it’s Slack or Facebook Messenger. Building a “Slackbot,” for example, gives your users another way to get help or find information within a familiar interface.

If your interaction with a conversational bot is through a specific menu (where you interact through buttons but the bot does not understand natural language input), chances are you are talking to a bot with structured questions and responses. This type of bot is usually applied on messenger platforms for marketing purposes. They are great at conducting surveys, generating leads, and sending daily content pieces or newsletters.
On the other hand, early adoption can be somewhat of a curse. In 2011, many companies and individuals, myself included, invested a lot of time and money into Google+, dubbed to be bigger than Facebook at the time. They acquired over 10 million new users within the first two weeks of launch and things were looking positive. Many companies doubled-down on growing a community within the platform, hopeful of using it as a new and growing acquisition channel, but things didn't exactly pan out that way.
Through Amazon’s developer platform for the Echo (called Alexa Skills), developers can develop “skills” for Alexa which enable her to carry out new types of tasks. Examples of skills include playing music from your Spotify library, adding events to your Google Calendar, or querying your credit card balance with Capital One — you can even ask Alexa to “open Dominoes and place my Easy Order” and have pizza delivered without even picking up your smartphone. Now that’s conversational commerce in action.

"From Russia With Love" (PDF). Retrieved 2007-12-09. Psychologist and Scientific American: Mind contributing editor Robert Epstein reports how he was initially fooled by a chatterbot posing as an attractive girl in a personal ad he answered on a dating website. In the ad, the girl portrayed herself as being in Southern California and then soon revealed, in poor English, that she was actually in Russia. He became suspicious after a couple of months of email exchanges, sent her an email test of gibberish, and she still replied in general terms. The dating website is not named. Scientific American: Mind, October–November 2007, page 16–17, "From Russia With Love: How I got fooled (and somewhat humiliated) by a computer". Also available online.

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