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2019 | Buch

Processes in the Banking and Insurance Industries Kapitel lesen Erstes Kapitel lesen

Building an Enterprise Chatbot

Work with Protected Enterprise Data Using Open Source Frameworks

verfasst von: Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Explore the adoption of chatbots in business by focusing on the design, deployment, and continuous improvement of chatbots in a business, with a single use-case from the banking and insurance sector. This book starts by identifying the business processes in the banking and insurance industry. This involves data collection from sources such as conversations from customer service centers, online chats, emails, and other NLP sources. You’ll then design the solution architecture of the chatbot. Once the architecture is framed, the author goes on to explain natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) with examples.

In the next sections, you'll design and implement the backend framework of a typical chatbot from scratch. You will also explore some popular open-source chatbot frameworks such as Dialogflow and LUIS. The authors then explain how you can integrate various third-party services and enterprise databases with the custom chatbot framework. In the final section, you'll discuss how to deploy the custom chatbot framework on the AWS cloud.

By the end of Building an Enterprise Chatbot, you will be able to design and develop an enterprise-ready conversational chatbot using an open source development platform to serve the end user.

What You Will LearnIdentify business processes where chatbots could be usedFocus on building a chatbot for one industry and one use-case rather than building a ubiquitous and generic chatbot Design the solution architecture for a chatbotIntegrate chatbots with internal data sources using APIsDiscover the differences between natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) Work with deployment and continuous improvement through representational learning

Who This Book Is ForData scientists and enterprise architects who are currently looking to deploy chatbot solutions to their business.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Processes in the Banking and Insurance Industries
Abstract
According to Darwin’s On the Origin of Species, it is not the most intellectual of the species that survives; it is not the strongest that survives; the species that survives is the one that is best able to adapt and adjust to the changing environment in which it finds itself. The same analogy can apply to enterprises and their survival opportunities in the 21st century. In this digital era, it is of utmost importance for enterprises to adapt to the latest trends and technology advancements. With this book, we intend to prepare you with an emerging skill of building chatbots in the financial services domain, with a specific use case of an insurance agent (replicable to a bank assistant as well).
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Chapter 2. Identifying the Sources of Data
Abstract
Chatbots are one more channel of providing conversational flows to customers. In the previous chapter, we discussed how the banking and insurance industries are structured and what kinds of interactions happen with the customers in those industries. There are many types of touchpoints a bank or insurer provides to customers in the day-to-day operations, starting from selling a new policy to settling escalations of claims. All these touchpoints are sources of data for building an AI Assistant, i.e., chatbot. In this chapter, we will start by introducing chatbot types and sources of data for training chatbots and then we will introduce the General Data Protection Regulation (GDPR) in context of the chatbot for personal data.
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Chapter 3. Chatbot Development Essentials
Abstract
Chatbots need to have features that enable human-like conversations. The goal is to make a chatbot conversation more human and thus better than the menu-driven approach of modern apps. In the previous chapters, we discussed types of chatbots and the regulatory constraints to consider for an in-house developed chatbot. In this chapter, we will discuss the simplified approach to building the integral components of chatbots. Later sections will introduce conversation flow for a sample request to facilitate the context build-up in a chatbot conversation. The chapter will end with the introduction to the “24x7 Insurance Agent” chatbot, which will be the use case discussed throughout the rest of the book.
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Chapter 4. Building a Chatbot Solution
Abstract
Chatbots are complete solutions and are created as an independent layer in any solution. The senior management also looks at chatbot functionalities and ROI as of an independent entity. The focus on conversational technologies further demands a holistic view on chatbots from solution and business value perspectives. In previous chapters, we demystified the essentials of developing a chatbot for a closed domain. In this chapter, we will focus on how to build solutions using the best available resources for a closed domain use case. The chapter will also cover a thought process on how to measure success for a chatbot implementation and managing the risks associated with chatbots.
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Chapter 5. Natural Language Processing, Understanding, and Generation
Abstract
The human brain is one of the most advanced machines when it comes to processing, understanding, and generating (P-U-G) natural language. The capabilities of the human brain stretch far beyond just being able to perform P-U-G on one language, dialect, accent, and conversational undertone. No machine has so far reached the human potential of performing all three tasks seamlessly. However, the advances in machine learning algorithms and computing power are making the distant dream of creating human-like bots a possibility.
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Chapter 6. A Novel In-House Implementation of a Chatbot Framework
Abstract
In previous chapters, we explained intents and different ways of classifying intents using natural language techniques. We also discussed the various data sources that are available in designing an enterprise chatbot. There are many chatbot builder platforms and frameworks available in the market that can be used to build chatbots. These frameworks abstract much complex functionality and provide components that are reusable, extendable, and scalable.
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Chapter 7. Introduction to Microsoft Bot, RASA, and Google Dialogflow
Abstract
In the previous chapter, we discussed how to build an in-house chatbot framework with natural language and conversation capabilities. Building a solution from scratch has advantages that we discussed previously. However, there are use cases and scope where it could be easier, quicker, and cheaper to use readily available online intent classification and conversation management frameworks to build your chatbot client.
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Chapter 8. Chatbot Integration Mechanism
Abstract
In Chapter 6, we designed a simple chatbot framework in Java which we called the IRIS (Intent Recognition and Information Service) framework. We discussed the core components of IRIS, such as how to define intents and how state machine can be implemented for defining state and transitions for building a conversational chatbot. An example use case focused on the insurance domain. In the example, we outlined specific capabilities that IRIS is supposed to perform such as providing market trend details, stock price information, weather details, and claim status.
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Chapter 9. Deployment and a Continuous Improvement Framework
Abstract
In the previous chapters, we designed a basic chatbot framework from scratch and explored integration options with third-party services and other backend systems. We also explained how to expose the IRIS chatbot framework as a Spring Boot REST API. In this chapter, we will discuss different ways in which IRIS can be deployed on a remote server. We will also discuss how to integrate IRIS with Alexa in less than 5 minutes. At the end of the chapter, we will discuss how IRIS can be extended to be part of a continuous improvement framework by implementing a self-learning module and bringing a human into the loop.
Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
Backmatter
Metadaten
Titel
Building an Enterprise Chatbot
verfasst von
Abhishek Singh
Karthik Ramasubramanian
Shrey Shivam
Copyright-Jahr
2019
Verlag
Apress
Electronic ISBN
978-1-4842-5034-1
Print ISBN
978-1-4842-5033-4
DOI
https://doi.org/10.1007/978-1-4842-5034-1