: Abhishek Singh, Karthik Ramasubramanian, Shrey Shivam
: Building an Enterprise Chatbot Work with Protected Enterprise Data Using Open Source Frameworks
: Apress
: 9781484250341
: 1
: CHF 55.90
:
: Anwendungs-Software
: English
: 399
: Wasserzeichen/DRM
: PC/MAC/eReader/Tablet
: PDF
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 ofBuilding 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 Learn
  • I entify business processes where chatbots could be used
  • Focus 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 chatbot
  • Integrate chatbots with internal data sources using APIs
  • Discover 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
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Who This Book Is For
Data scientists and enterprise architects who are currently looking to deploy chatbot solutions to their business.


Abhishek Singh is on a mission to profess the de facto language of this millennium, the numbers. He is on a journey to bring machines closer to humans, for a better and more beautiful world by generating opportunities with artificial intelligence and machine learning. He leads a team of data science professionals solving pressing problems in food security, cyber security, natural disasters, healthcare, and many more areas, all with the help of data and technology. Abhishek is in the process of bringing smart IoT devices to smaller cities in India so that people can leverage technology for the betterment of life.

He has worked with colleagues from many parts of the United States, Europe, and Asia, and strives to work with more people from various backgrounds. In 7 years at big corporations, he has stress-tested the assets of U.S. banks at Deloitte, solved insurance pricing models at Prudential, and made telecom experiences easier for customers at Celcom, and core SaaS Data products at Probyto. He is now creating data science opportunities with his team of young minds.

He actively participates in analytics-related thought leadership, authoring, public speaking, meetups, and training in data science. He is a staunch supporter of responsible use of AI to remove biases and fair use of AI for a better society.

Abhishek completed his MBA from IIM Bangalore, a B.Tech. In Mathematics and Computing from IITGuwahati, and a PG Diploma in Cyber Law from NALSAR University, Hyderabad.


< >Karthik Ramasubramanian has over seven years of practice and leading Data Science and Business Analytics in Retail, FMCG, E-Commerce, Information Technology for a multi-national and two unicorn startups. A researcher and problem solver with a diverse set of experience in the data science lifecycle, starting from a data problem discovery to creating a data science prototype/product.

On the descriptive side of data science, designed, developed and spearheaded many A/B experiment frameworks for improving product features, conceptualized funnel analysis for understanding user interactions and identifying the friction points within a product, designing statistically robust metrics and visual dashboards. On the predictive side, developed intelligent chatbots which understand human-like interactions, customer segmentation models, recommendation systems, identifying medical specialization from a patient query for telemedicine, and many more.

He actively participates in analytics related thought leadership, authoring blogs& books, public speaking, meet-ups, and training& mentoring for Data Science.

 Kart ik completed his M.Sc. in Theoretical Computer Science at PSG College of Technology, India, where he pioneered the application of machine learning, data mining, and fuzzy logic in his research work on the computer and network security.


Shrey Shivam extensive experience in leading the design, development, and delivery of solutions in the field of data engineering, stream analytics, machine learning, graph databases, and natural language processing. In his seven years of experience, he has worked with various conglomerates, startups, and big corporations and has gained relevant exposure to digital media, e-commerce, investment banking, insurance, and a suite of transaction-led marketplaces across music, food, lifestyle, news, legal and travel.

 

He is a keen learner and is actively engaged in designing the next generation of systems powered by artificial intelligence-based analytical and predictive models. He has taken up various roles in product management, data analytics, digital growth, system architecture, and full stack engineering. In the era of rapid acceptance and adoption of new and emerging technologies, he believes in strong technical fundamentals and advocates continuous improvement through self-learning.

 

Table of Contents5
About the Authors12
About the Technical Reviewer15
Acknowledgments16
Introduction18
Chapter 1: Processes in the Banking and Insurance Industries20
Banking and Insurance Industries20
A Customer-Centric Approach in Financial Services25
Benefits from Chatbots for a Business28
Chatbots in the Insurance Industry29
Automated Underwriting31
Instant Quotations32
AI-Based Personalized Experience32
Simplification of the Insurance Buying Process32
Registering a Claim32
Finding an Advisor32
Answering General Queries33
Policy Status33
Instant Notifications33
New Policy or Plan Suggestions33
Conversational Chatbot Landscape33
Summary36
Chapter 2: Identifying the Sources of Data38
Chatbot Conversations38
General Conversations39
Specific Conversations39
Training Chatbots for Conversations40
Self-Generated Data41
Customer Interactions42
Phone43
Emails43
Chat43
Social Media43
Customer Self-Service44
Mobile44
Customer Service Experts44
Open Source Data45
Crowdsourcing45
Personal Data in Chatbots46
Introduction to the General Data Protection Regulation (GDPR)48
Data Protected Under the GDPR48
Data Protection Stakeholders49
Customer Rights Under the GDPR49
Chatbot Compliance to GDPR51
Summary52
Chapter 3: Chatbot Development Essentials53
Customer Service-Centric Chatbots53
Business Context54
Policy Compliance56
Security, Authentication, and Authorization57
Accuracy of User Input Translation to Systems59
Chatbot Development Approaches60
Rules-Based Approach