ACCT 597: Tax Transformation and Technology Final Project

BRIDGING THE GAP: AI'S TRANSFORMATIVE ROLE IN

MODERN TAXATION Fall 2024 – 597 Final Paper

Dai, Annie [email protected]

Presented to Professor Jeffrey Bogan at California State University, Fullerton

In Partial Fulfillment of the Requirements for the Degree Master of Science In Taxation

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Contents I. Introduction ………………………………………………………………………………………. 3

Objectives of the Paper ……………………………………………………………………………………………. 3 Overview of Artificial Intelligence (AI) …………………………………………………………………………. 3 What is AI? …………………………………………………………………………………………………………….. 4 The Components and Branches of AI …………………………………………………………………………. 5

1. 1. Learning …………………………………………………………………………………….. 5

2. 2. Reasoning and Decision Making …………………………………………………….. 5

3. 3. Problem-Solving ………………………………………………………………………….. 6

4. 4. Perception ………………………………………………………………………………….. 7

5. 5. Language-Understanding ……………………………………………………………… 7

The History of Artificial Intelligence and Its Impact Today ………………………………………………. 8 II. Theoretical Foundations of AI in Tax ……………………………………………………. 10

AI and Its Technologies in Taxation ……………………………………………………………………………10

6. Machine Learning ………………………………………………………………………….. 10

7. Data Mining ………………………………………………………………………………….. 12

8. Artificial intelligence ……………………………………………………………………….. 16

9. Natural Language Processing (NLP) …………………………………………………. 17

AI in the Context of Digital Transformation ………………………………………………………………….18

10. The role of AI in the broader context of digital transformation in tax ………… 19

11. Comparison with Traditional Tax Technologies …………………………………… 19

Legal and Ethical Considerations ………………………………………………………………………………21

12. Legal Frameworks Governing AI in Tax …………………………………………….. 22

13. Ethical Considerations and Challenges ……………………………………………… 23

III. AI Applications in Taxation …………………………………………………………………. 25

14. AI in Tax Compliance and Reporting …………………………………………………. 26

15. Automation of Tax Filing and Reporting Processes ……………………………… 29

16. AI-Powered Tax Preparation Software ………………………………………………. 29

17. Real-Time Data Analytics for Compliance Monitoring …………………………… 30

AI in Tax Planning and Strategy ………………………………………………………………………………..33

18. Predictive Analytics for Tax Planning ………………………………………………… 33

19. AI Tools for Optimizing Tax Strategies ………………………………………………. 36

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AI in Tax Audits and Investigations …………………………………………………………………………….37

20. AI in Detecting Tax Fraud and Evasion ……………………………………………… 37

21. Use of AI in Risk Assessment and Audit Selection ………………………………. 38

22. Examples of AI Applications in Government Tax Agencies ……………………. 38

IV. Impact of AI on the Role of Tax Professionals ……………………………………….. 39

Changing Skill Sets and Competencies ………………………………………………………………………40 AI as A Tool for Enhanced Decision-Making ………………………………………………………………..42 The Future of Tax Jobs ……………………………………………………………………………………………42

V. Challenges and Risks of AI in Taxation…………………………………………………. 43

Technical Challenges ………………………………………………………………………………………………43 Legal and Regulatory Challenges ………………………………………………………………………………44 Ethical and Social Risks …………………………………………………………………………………………..45

VI. The Future of AI in the Tax Profession ………………………………………………….. 47

Emerging Trends in AI and Taxation ………………………………………………………………………….48 Preparing for the Future …………………………………………………………………………………………..49

VII. Conclusion ………………………………………………………………………………………. 50

Implications for the Future of Tax Professionals …………………………………………………………..50

23. Long-Term Implications of AI in the Tax Field …………………………………….. 50

Final Thoughts ………………………………………………………………………………………………………..51 Bibliography ……………………………………………………………………………………………………. 52

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I. Introduction

In today’s rapidly evolving landscape, the integration of artificial intelligence (AI)

in tax has become a transformative force, reshaping the interplay between business,

data, and taxation. This paper aims to explore the complex challenges tax professionals

face in leveraging AI technologies, providing an in-depth examination of the technical

and operational limitations encountered in practical scenarios.

Objectives of the Paper

By delving into these challenges, the paper will propose strategic advancements

and AI-driven enhancements to navigate the intricate and dynamic world of tax

compliance, planning, and reporting. The objective is to offer actionable insights and

innovative solutions that empower tax professionals to address nuanced, multifaceted

issues with greater accuracy, efficiency, and foresight, ensuring alignment with the ever-

changing demands of the global tax environment.

This exploration seeks to bridge the gap between traditional tax methodologies

and cutting-edge AI capabilities, paving the way for a more effective and data-driven

approach to taxation.

Overview of Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence in

machines designed to perform tasks that typically require human cognitive abilities,

such as learning, reasoning, problem-solving, perception, and decision-making. By

leveraging advanced algorithms, machine learning, and vast amounts of data, AI

systems can adapt and improve over time, driving innovation across industries. From

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automating mundane tasks to enabling predictive insights and fostering creativity, AI is

transforming how we interact with technology and navigate the complexities of modern

life.

What is AI?

Before diving into the topic, it’s important to understand what AI, or artificial

intelligence, truly means. According to the Collins English Dictionary, "artificial" refers to

something created by humans rather than occurring naturally, often designed to imitate

a natural product. (Collins Dictionaries) "Intelligence," on the other hand, is defined as

the capacity for learning, reasoning, understanding, and other mental activities. (Collins

Dictionaries) Combining these definitions, we can understand AI as the effort to create

human-made systems or products endowed with intelligence designed to mimic human

thought processes.

According to Google Cloud, artificial intelligence (AI) refers to a collection of

advanced technologies that empower computers to perform complex tasks that typically

require human intelligence. These tasks include visual perception, language

understanding and translation, data analysis, and generating actionable

recommendations. AI is not a standalone concept; it is the result of integrating multiple

disciplines, such as computer science, data analytics, statistics, software engineering,

and many other fields, working together to create intelligent systems capable of solving

real-world problems.

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The Components and Branches of AI

After learning the history and definition of AI, we can take a deep dive into the AI

world, where we first need to understand what the 5 important components of artificial

intelligence are.

1. Learning

Learning in AI resembles how humans acquire knowledge, where individuals

memorize information and apply it when needed. As stated, “One of the essential

components of AI, learning for AI includes the trial-and-error method. The solution keeps

on solving problems until it comes across the right results.” (Bansal) The learning

process involves memorizing various aspects of information, such as solutions to

problems, vocabulary, and terms from different languages. This process, known as rote

learning, is foundational and often evolves into the generalization method. “This learning

method is later implemented using the generalization method.” (Bansal)

2. Reasoning and Decision Making

AI understands information and makes decisions using reasoning. This means it

figures things out based on what it knows. There are two types of reasoning: inductive

and deductive. Deductive reasoning gives clear and definite answers, while inductive

reasoning is used in situations where the results are uncertain and not guaranteed.

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3. Problem-Solving

AI solves problems using techniques such as planning, searching, and

optimization. These methods help AI find the best way to reach a solution. Some

techniques are special-purpose methods, which are designed to handle specific types of

Figure 1

Components of AI. Image from "What are the Components of AI?" ellow.io, 11 Jan. 2024,

https://ellow.io/components-of-ai/.

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problems by focusing on their unique features. Others are general-purpose methods,

which are more flexible and can be applied to a variety of different problems. Problem-

solving in AI often works by breaking the task into smaller steps. This process involves

gradually reducing the differences between the current situation (where things are now)

and the goal (the desired outcome) until the problem is completely solved.

4. Perception

AI understands its surroundings by collecting and interpreting information, similar

to how humans use their senses. It processes this data to study scenes, recognize

objects, and figure out how they relate to each other and their features. This ability,

called perception, is very important for tasks like understanding images and recognizing

speech.

5. Language-Understanding

"In simpler terms, language can be defined as a set of different system signs that

justify their means using convention. As one of the widely used artificial intelligence

components, language understanding uses distinctive types of language over different

forms of natural meaning, exemplified overstatements.” (Bansal) AI processes

language by understanding and creating natural language. It uses techniques such as

natural language understanding, machine translation, and text generation to work with

language effectively. These capabilities power tools like chatbots, translation apps, and

sentiment analysis, allowing them to perform smoothly and interact naturally.

Moreover, artificial Intelligence is divided into five main branches, each playing a

unique role in creating intelligent systems. Machine Learning (ML) focuses on enabling

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machines to learn from data and algorithms without needing explicit programming,

improving adaptability and decision-making. Deep Learning (DL), a subset of ML, uses

neural networks inspired by the human brain to analyze complex data features and

perform tasks like language processing in tools like Alexa or Google Home. Natural

Language Processing (NLP) allows AI to understand and interpret spoken and written

language, making technologies like chatbots, virtual assistants, and sentiment analysis

possible. Robotics combines AI with physical machines to create robots capable of

performing tasks autonomously in industries like healthcare and manufacturing. Lastly,

Fuzzy Logic helps AI handle uncertainties by working with "if-then" rules, improving

decision-making in real-world scenarios, such as determining braking force in automatic

systems. Together, these branches build the foundation for AI’s versatile applications.

The History of Artificial Intelligence and Its Impact Today

AI has become a widely discussed topic today, but its origins trace back much

further, with foundational concepts emerging in the early 20th century. In 1921, Czech

playwright Karel Čapek introduced the term "robots" in his science fiction play,

presenting the idea of "artificial people" to the public. By the mid-20th century, the field

took a significant leap when Alan Turing published his seminal paper, "Computing

Machinery and Intelligence" (Turing), which introduced the concept of the Turing Test

(Oppy and Dowe), originally referred to as the "imitation game." This test became the

benchmark for assessing machine intelligence. It was during this period that the term

"artificial intelligence" was officially coined, marking the formal beginning of AI as a field

of study.

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The evolution of AI has seen both highs and lows over the decades. The late

1980s, known as the "AI Boom," marked a period of rapid advancements bolstered by

government support and breakthroughs in research. However, this momentum was

short-lived, as the "AI Winter" (1987-1993) brought a decline in interest and funding due

to high costs and limited returns, along with setbacks in expert systems and strategic

computing projects. Despite this, the 1990s to early 2000s saw significant progress,

including AI’s entry into everyday life with innovations like speech recognition software

and the Roomba, as well as groundbreaking achievements such as an AI defeating a

world chess champion. From 2012 to the present, advancements in artificial general

intelligence have transformed AI into a common part of daily life, popularizing

technologies like virtual assistants, search engines, deep learning, and big data.

According to Wolters Kluwer, “Today, artificial intelligence is transforming the way

tax and accounting professionals do their work. Rapid advancements in AI are making it

easier than ever for accounting firms to speed up routine tasks, analyze massive

amounts of financial data, spot patterns and anomalies, reduce human error, and keep

up with changing laws and regulations.” (Wolters Kluwer) This transformation allows tax

and accounting professionals to focus more on strategic decision-making and advisory

roles rather than spending time on repetitive, manual tasks. AI-powered tools not only

enhance efficiency but also improve accuracy, enabling firms to provide better insights

and value to their clients. As regulations become increasingly complex and data

volumes grow, integrating AI into tax and accounting workflows is becoming essential

for staying competitive and ensuring compliance in a rapidly evolving industry.

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II. Theoretical Foundations of AI in Tax

AI and Its Technologies in Taxation

When it comes to Tax world, we heard so much about data analytics, but in the details,

we need understanding the some of key components that make data analytics famous

which is Machine Learning, data mining, artificial intelligence and more.

Machine Learning

Over the past decade, Artificial Intelligence (AI) and Machine Learning (ML)

technologies have revolutionized the tax landscape, transforming how tax authorities

and businesses operate. These tools are not a threat but a significant opportunity to

enhance efficiency, reduce costs, and improve outcomes. Governments and

organizations worldwide are increasingly adopting AI and ML to modernize tax

compliance and fraud detection processes.

Machine learning, often misunderstood due to its portrayal in popular media, is a

practical tool that uncovers hidden patterns in large datasets and progressively

improves its performance. Instead of mimicking human intelligence, its purpose is to

provide actionable insights, reducing fraud, waste, and abuse across various industries,

including tax administration. Tax agencies leverage machine learning techniques to

strengthen fraud detection, streamline audits, and reduce biases in taxpayer

evaluations. Supervised learning, for example, uses past cases of fraud and audits to

identify patterns and correlations, enabling systems to predict similar fraudulent

activities in the future. This approach helps auditors focus on high-risk areas while

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learning from both successes and failures, improving the overall accuracy of audits and

reducing biases.

Unsupervised learning, on the other hand, explores unknown areas by identifying

outliers or anomalies without prior case data. Techniques like clustering group similar

tax returns, flagging outliers for further investigation. This approach is particularly

valuable in uncovering high-risk taxpayer segments or non-compliant activities that

traditional methods often overlook. Both supervised and unsupervised learning

Figure 2

Types of machine learning. (Oluwole, "What Are the Types of Machine Learning?")

https://python.plainenglish.io/what-are-the-types-of-machine-learning-540b15dc467f

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complement each other, providing tax authorities with robust tools for fraud detection

and compliance. Supervised learning enhances the detection of known fraud patterns,

while unsupervised learning uncovers new, hidden risks.

Machine learning’s adaptability allows tax agencies to create dynamic models that

evolve with new data. This “feedback loop” continuously refines fraud detection

capabilities and ensures predictive models remain accurate over time. These systems

can even alert auditors when models lose accuracy, ensuring continuous optimization.

While machine learning cannot eliminate all fraud, it significantly strengthens the fight

against tax evasion and abuse. By reducing fraud, governments can reclaim funds that

rightfully belong to the public, ensuring they are used to improve programs and

services. As tax systems grow increasingly complex, machine learning provides a

powerful, evolving tool to close gaps and protect public funds.

Data Mining

Tax risk detection using data mining and AI technologies has emerged as a critical focus

area in modern tax administration. The ability to extract insights from vast datasets has

made data mining indispensable for improving tax compliance, identifying irregularities,

and mitigating risks. As illustrated in Fig. 3, the growing body of research highlights the

increasing interest among scholars and practitioners in leveraging data mining for tax

risk detection. Over the past two decades, the number of published studies has

exhibited a consistent upward trend, emphasizing the importance of this field in shaping

the future of tax administration.

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This increased attention reflects the significant benefits data mining offers to tax

administrations. By employing advanced techniques, tax authorities and professionals

can streamline the process of identifying risks, understanding challenges, and

uncovering opportunities. These capabilities allow for more efficient and proactive tax

compliance strategies, reducing manual effort and improving accuracy. Moreover, the

application of data mining provides a clearer picture of taxpayer behavior, enabling

authorities to focus resources on high-risk cases while minimizing unnecessary audits.

Figure 3.

Research and development trends of tax risk detection.

(Zheng et al., "A Survey of Tax Risk Detection Using Data Mining Techniques")

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Data mining in tax risk detection is divided into two primary branches, as illustrated in

Fig. 4: Non-Relationship-Based Methods and Relationship-Based Methods. Each

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