Introduction
Contents:
Tentative titles of the chapters of the book and their brief descriptions are given below:
1. Introduction: Robotic Process Automation aims to automate business processes. The objective is
to eliminate or reduce human interference in routine business specific activities. This automation is
carried out by software bots that use predefined rules to process the tasks. Some routine tasks that
are automated include reading the files, filling out forms, creating and arranging files etc. RPA has
been proven to increase efficiency and accuracy resulting in making it one of the fastest emerging
fields. This chapter provides the emergence of RPA, its historical perspective, needs, benefits,
challenges etc. and makes the reader aware of the basic information about RPA.
2. RPA Development: As with any other software, RPA development also must go through the
complete development life cycle. RPA bots differ from traditional software as they are capable of
decision making supported by the attached rule set. This chapter includes important phases like
Feasibility of RPA, Planning, Development of rule set and RPA bots. It also discusses the
challenges faced in development. The chapter emphasizes the most important steps of rules
generation and bot development.
3. RPA Testing, Deployment and Scalability: RPA bots differ from the traditional software as they
mimic decision making capabilities due to their rule-based approach. Testing of these bots also
needs real world data for rigorous testing. This chapter emphasizes the collection of such data. RPA
bots’ deployment and monitoring becomes more crucial if implemented for critical operations. The
true objective of automation can be obtained only if the RPA systems can be scaled to achieve
mass automation. This chapter discusses approaches, issues and technical insights of RPA testing,
deployment and scalability.
4. Intelligence in RPA: With the advance of Artificial Intelligence, systems are more and more
equipped with decision making capabilities. Instead of depending upon a user defined set of rules,
Intelligent RPA is trained using machine learning methods to take decisions while processing
business operations. Continuous advancements in Artificial Intelligence field have also contributed
and made RPA more efficient and accurate. Intelligent RPA typically follow preparing training data,
training the RPA bots and testing the systems for accuracy. This chapter discusses all these critical
issues along with some selected machine learning algorithms suitable for implementation.
5. Natural Language Processing for capability enhancement of RPA: Natural Language
Processing (NLP) enables machines to process text data. This includes the capability of accepting
text input, processing it and generating relevant response, if required. NLP enabled RPA systems
can acquire additional capabilities like interacting with users and handling runtime input. NLP
equipped RPA systems can act as fully automated standalone systems to handle complete business
transaction cycle for most of the applications. This chapter discusses major aspects of NLP
integration in RPA and some use cases of such systems.
6. Generative AI as a tool for RPA: Generative Artificial Intelligence (AI) is the capacity of systems to
generate image, video, text, audio and other digital content. All businesses involving content
creation need Generative AI enabled RPA systems for complete automation. As an example, a
tutoring system will be much more effective if it can provide on demand relevant study material to
the learner in addition to managing the routine learning activities. This chapter discusses general
aspects of Generative AI, provides some use cases of generative AI enabled RPA systems and
brainstorms technical aspects of the integration of both the technologies.
7. Exploiting cloud in RPA: Cloud assisted RPA provides the flexibility to control the automated
processes from anywhere. Inbuilt storage, computation and security facilities over cloud can deliver
extremely efficient, accurate and accessible RPA systems. RPA as a cloud service provides
unmatched scalability to the RPA service providers. After discussing the major considerations of
cloud assisted RPA, the chapter examines the increased cost and benefits of the combination of
both the services. The chapter also analyzes major tools for cloud-based implementation of RPA
like Automate Anywhere.
8. Analysis of RPA Development tools: RPA is a broad field thus there is a vast canvas of tools
available for development of these systems. Selection of the tools requires specific categorization of
the requirements of the system. Tools are available for development of simple rule-based RPA,
Intelligent RPA and self-learning RPA systems. This chapter provides details on identifying the
system types and then it discusses the considerations for selecting the best suited tools from the
available range. Then the chapter makes its readers friendly with some of the popular tools including
Blue Prism and UiPath.
9. Intrinsic Vulnerabilities of RPA systems: Complex business processes result in complex RPA
adaptations. Complexity comes with expanded vulnerability. Probable points of failures multiply with
each added functionality. Single point failure can stall the whole chain of associated automated
processes. In addition to reliability, security of these systems is also a major concern for
organizations. Each security vulnerability exposes the whole system to threats. This chapter
explores the reliability issue and steps to counter the same. It also analyzes the enhanced threat
landscape and improvised security countermeasures for the vulnerable RPA systems.
10. RPA in Healthcare: Healthcare industry has been one of the largest beneficiaries of computer
aided automation. For example, the use of image processing techniques in diagnosis using
radiographs, CT scans and MRI scans is realistically assisting human expertise. The complexity and
expertise required for decision making in diagnosis, intervention and care leads to further scope for
automation in the area. RPA systems have the potential to automate the complete life cycle of
healthcare. It can automate the components like routine check-ups, diagnosis, advising suitable
procedures and prescriptions, assisting in post procedure care etc. This chapter reviews the existing
RPA implementations in healthcare. Then it presents a standard methodology for applying RPA in
the field. Furthermore, it explores the possibility of lifecycle automation of novel healthcare
processes.
11. RPA in Banking & Finance: Advance of computer and mobile technology has empowered a naïve
banking customer to perform critical financial transactions from the comfort of their chairs. The
sector has numerous processes that can be completely automated by applying RPA. Examples
covered in this chapter include processing credit applications, approaching and engaging new
customers, performing financial transactions, generating reports and taking suitable actions. The
chapter also discusses the potential novel candidate processes for RPA deployment.
12. RPA for secure Systems and Networks: Every attack on computer systems and networks has its
footprints. Security systems can be trained to analyze these footprints and avoid future attacks.
These advance signals can come from several sources like antivirus systems, firewalls, system logs
and intrusion detection systems. RPA implementation in security systems automates the complete
life cycle of security including monitoring, detection, analysis and preventive and reactive processes.
In addition to discussing the complete life cycle automation of systems and network security, the
chapter also briefly explores the RPA in web applications security.
13. RPA in Education: The education field comprises two sets of processes namely administrative and
academic. Administrative processes involve activities that are easy to automate and are like
automation of any other business process. Some examples of these activities are enrolling students,
processing grades, generating report cards, preparing schedules and managing financial
transactions. On the other hand, academic processes are difficult to automate and need expert
systems with powerful cognitive capabilities. This chapter first reviews the utilization of RPA in
academic processes. Then it provides a framework suggesting steps for maximum automation of
these processes.
14. RPA for IoT enabled applications: Internet of Things (IoT) is a network of sensors, logic and
actuators. Sensors sense the underlying environment to gather actionable information. Logic
components make a rule-based decision and actuators react to maintain the preferred environment.
There is a vast landscape for IoT applications. Many a times the logic component needs either
human intervention or is dependent on an external entity. RPA automation can eliminate this human
or external dependance. The chapter examines the IoT as a use-case for RPA. It also covers a few
specific examples of complete automation of IoT enabled business processes.
15. Future Challenges and Opportunities for Intelligent RPA Systems: This chapter concludes the
book while exploring the novel fields for the combination of AI and RPA. It covers some of the most
advanced and innovative use cases. A detailed discussion on challenges include cost, time
constraints and availability of expertise. A summary of the concepts covered in the entire book is
likely to be useful for the readers.