Table of Contents
Artificial Intelligence Translates the Language of Animals
Kim Kyung-jin, Attorney at Law
The Story of AI Learning to Listen to Whales, Dolphins, Birds, and Bees
Twelve chapters on how AI listens to dolphins, sperm whales, humpback whales, birds, and bees to find rules in their sounds, and what this technology means for its risks and for animal rights. Written in simple sentences a child can read, with verified sources in every section.
Table of Contents
AI Deciphers Ancient Scripts
Kim Kyung-jin, Attorney at Law
Ancient Records Revived by AI
In twelve chapters, this book explains how AI revives records once unreadable, from burned scrolls and wooden slips buried in mud to broken clay tablets. It covers virtual unrolling at Herculaneum, virtual collation of oracle-bone texts, reading Silla wooden tablets, computational analysis of undeciphered scripts, and multispectral archives, with verified references for each chapter.
Table of Contents
Artificial Intelligence for New Materials Design and Rocket Propulsion Engineering
Kim Kyung-jin, Attorney at Law
AI Potentials, Self-Driving Laboratories, and Physics-Informed Machine Learning (PIML)
Ten chapters on how artificial intelligence is changing new materials and rocket propulsion: atomic simulation, generative models, self-driving labs, high-temperature alloys, metal 3D printing, combustion, cooling design, and engine diagnosis and control. Written without equations, with verified sources in every chapter.
AI Library
The Age of Autonomous Scientific Discovery
Kim Kyung-jin, Attorney at Law
AI Scientists and Self-Driving Labs
This book follows how AI scientists and self-driving labs are changing the way science generates and verifies claims. It covers literature-based discovery, natural-language protocols translated into robot commands, multi-agent research systems, closed-loop laboratories, materials search, the verification gap, chains of evidence, research harnesses, journal ethics, and legal responsibility.
AI Library
A New Era of Life Sciences Opened by Artificial Intelligence
Structural Proteomics, Genomic Foundation Models, Autonomous Laboratories, and Global Governance
Kim Kyung-jin, Attorney at Law
This book is a research volume compiled with artificial intelligence. A human selected the materials and structured the work, while AI models drafted the sentences and cross-checked the facts.
AI Library
The Double Structure of Digital Sovereignty
Europe’s Departure from Palantir and the Chains of American Big Tech
Kim Kyung-jin, Attorney at Law
This is a record of 2026, when European intelligence agencies and defense ministries began removing analytics tools from America’s Palantir. It covers the replacement decisions made by France’s General Directorate for Internal Security (DGSI), Germany’s Federal Office for the Protection of the Constitution (BfV), and the Netherlands Ministry of Defense; the incident in which US export controls severed an ally’s ac…
New English Edition
Artificial Intelligence in Horticulture
Kim Kyung-jin, Attorney at Law
Across five chapters and ten sections, this book examines computer vision for crop diagnosis, harvesting robots and autonomous field systems, smart greenhouses and digital twins, precision irrigation and supply-chain quality control, high-throughput phenotyping, and predictive breeding.
New English Edition
Artificial Intelligence in Food Crop Agriculture
Kim Kyung-jin, Attorney at Law
Across six chapters and eighteen sections, the book examines digital agricultural infrastructure, remote sensing, crop diagnosis, yield forecasting, precision irrigation, genomics, molecular breeding, agricultural robotics, climate-smart agriculture, and global food security.
New English Edition
The Future of Forestry and Agroforestry
Kim Kyung-jin, Attorney at Law
Driven by Artificial Intelligence and Digital Innovation
Across five chapters and fifteen sections, the book follows satellites, drones, LiDAR, digital twins, forest-specific language models, wildfire and pest forecasting, forestry robotics, agroforestry, timber traceability, and forest carbon markets.
New English Edition
Smart Livestock Farming: AI Enters the Barn
Kim Kyung-jin, Attorney at Law
Sensors listen, cameras watch, and artificial intelligence helps farmers decide.
Across five chapters and fifteen sections, the book follows precision livestock farming from animal health and reproduction to robotic milking, virtual fencing, digital twins, methane reduction, welfare, and data ownership.
Table of Contents
Han Dong-hoon, Busan Buk-gu Gap: A Record of the 100 Days Before and After the Election (Mar. 26-Jul. 3, 2026)
Kim Kyung-jin
Table of Contents and 13 sections
From March 26 to July 3, 2026, this record follows the spring after expulsion, the Busan Buk-gu Gap by-election, victory as an independent, and the first bill submitted in the National Assembly.
Table of Contents
Artificial Intelligence and Medicine
Kim Kyung-jin, Attorney at Law
AI in clinical care, hospitals, education, and research
AI in medical imaging, risk prediction, treatment planning, hospital operations, education, and research, with patient safety, privacy, and accountability.
[AI Library] Chapter 9: Finance and Tax Administration
Global Case Studies in Introducing AI into Public Administration
Chapter 9: Finance and Tax Administration
Kim Kyung-jin
Chapter 9: Finance and Tax Administration
Finance and tax administration are critical areas that form the foundation of national operations. The government's ability to collect taxes properly and use the budget efficiently is the basis of all public services. Introducing artificial intelligence to this field can increase the accuracy of tax collection, reduce tax evasion, and create more efficient fiscal policies. In this chapter, we will look at representative cases of using artificial intelligence in finance and tax administration in various countries around the world.
1. AI-Based Tax Return Verification by the Inland Revenue Authority of Singapore
Singapore is a small nation but is famous for its highly efficient administrative system. The Inland Revenue Authority of Singapore (IRAS) began developing a tax return verification system using artificial intelligence. Previously, tax officials had to manually review many tax returns, which was time consuming and resulted in missing some inaccurate filings. Furthermore, even honest taxpayers sometimes submitted incorrect information by mistake due to complex tax regulations. To solve these problems, IRAS introduced AI technology to increase the accuracy of tax returns, reduce the workload of tax officials, and provide better services to taxpayers.
Singapore's AI-based tax return verification system has three main core functions.
First is the anomaly detection function. This system compares past tax return data with currently submitted filings to find unusual patterns or inconsistencies. For example, it can detect abnormally high deduction claims relative to income or sudden changes in income. To achieve this, machine learning algorithms learn millions of past tax return data points to identify "normal" filing patterns.
Second is the automatic classification function. The AI system automatically classifies submitted tax returns according to risk levels. They are divided into high risk cases requiring detailed review by tax officials, medium risk cases requiring review of only some items, and low risk cases that can be processed without additional review. This allows for the efficient allocation of limited personnel.
Third is the real-time feedback function. When a taxpayer files taxes online, the AI system reviews the input in real time and immediately informs them of potential errors or omissions. For example, it provides guidance such as, "Compared to last year, the child education expense deduction has been omitted; please check."
It utilizes various data sources. It integrates and analyzes past tax filing history, bank transaction data, real estate transaction records, and corporate accounting information. It also uses natural language processing (NLP) technology to understand and analyze attached documents or descriptions.
Several positive changes have emerged since the introduction of the AI system at the Inland Revenue Authority of Singapore.
First, the work efficiency of tax officials has significantly improved. Previously, one tax official could review about 30 tax returns per day, but after the AI system was introduced, they can process more than 100 cases. The system automatically processes low risk cases, allowing officials to focus on more complex and important cases.
Second, the accuracy of tax returns has improved. Thanks to the AI system's real-time feedback, taxpayers can immediately correct errors during the filing process. Additional processing costs due to incorrect filings have decreased by approximately 25%.
Third, there was an effect of increasing tax revenue. More accurate tax filing and effective discovery of omitted taxes increased annual tax revenue by approximately 200 million Singapore dollars (about 180 billion won). It has become possible to more effectively detect complex tax evasion attempts by high income earners and corporations.
Fourth, taxpayer satisfaction has improved. Thanks to real-time feedback and personalized guidance, many taxpayers rated the tax filing process as easier and more transparent. According to a 2022 survey, taxpayer satisfaction rose from 72% before the AI system introduction to 85% after.
The Inland Revenue Authority of Singapore plans to introduce more advanced AI technologies. Starting in 2023, it began providing interactive consultation services for taxpayers using Large Language Models (LLMs), and is developing a real-time transaction analysis and tax collection system by combining blockchain technology with AI.
2. Detection of Tax Evasion Patterns by HM Revenue and Customs
HM Revenue and Customs (HMRC) in the UK strives to reduce the "tax gap" (the difference between the amount of tax that should be collected and the amount actually collected) by tens of billions of pounds every year. In 2019, HMRC expanded the introduction of "Connect," an advanced AI-based tax evasion detection system, as a way to solve this problem.
The main objectives of this system were threefold. First, it was to effectively find complex and sophisticated tax evasion methods. Second, it was to focus limited investigation personnel on the most suspicious cases. Third, it was to establish a proactive tax evasion prevention system.
The "Connect" system is one of the largest data analysis platforms in the UK, applying various AI technologies.
The UK tax authority's Connect system integrates and analyzes more than 30 different data sources, including tax return data (VAT, self-assessment, local taxes), bank transaction information from the UK and 60 overseas countries, credit and debit card usage history such as Visa and Mastercard, real estate transaction records from the Land Registry, vehicle registration information from the DVLA, overseas asset information, activities on public social media accounts, sales records from online marketplaces like eBay or Airbnb, company information from Companies House, e-commerce sites, cryptocurrency exchanges, sharing economy platforms, credit rating agencies, and savings, pension, and investment records. HMRC holds more than 55 billion items of taxpayer data through this system, using it to effectively identify inconsistencies between tax filings and actual economic activities and detect cases with potential for tax avoidance or evasion.
The core functions of "Connect" are network analysis and pattern recognition. This system visualizes and analyzes complex relationships between individuals or companies to find hidden links. For example, it can detect cases where income is hidden through multiple layers of corporations or asset distribution through family members.
Through network analysis, the Connect system can visualize and analyze complex relationships between individuals or companies, making it possible to identify hidden links such as income concealment through multiple layers of corporations or asset distribution through family members.
The pattern recognition function involves AI algorithms learning "normal" financial activity patterns and automatically flagging any anomalies found, such as an unusually high standard of living compared to reported income. The predictive analysis function learns patterns from past tax evasion cases to identify new cases showing similar patterns in advance, allowing for the prediction and focused management of individuals or companies at high risk of potential tax evasion. Notably, there is a document analysis function utilizing natural language processing (NLP) technology. The natural language processing function automatically analyzes various documents such as returns, emails, and contracts to efficiently find inconsistencies or suspicious content.
Since the introduction of the "Connect" system, HMRC has achieved various results.
First, the tax evasion detection rate has significantly improved. As of 2020, the success rate of tax evasion investigations through the "Connect" system was approximately 88%, a significant improvement over the 65% success rate of previous manual investigations.
Second, there was an effect of securing additional tax revenue. From 2019 to 2022, approximately 3.7 billion pounds (about 6 trillion won) in additional tax revenue was secured from tax evasion caught through the "Connect" system.
Third, investigation efficiency has improved. By accurately identifying high risk cases, the AI system has allowed investigators to handle more tax evasion cases with less time and resources. Average investigation time has been reduced by about 30%.
Fourth, there was a preventive effect. As the tax authority's advanced tax evasion detection capabilities became known, a strong deterrent effect occurred for potential tax evaders. According to a 2021 survey, approximately 67% of taxpayers responded that they decided not to attempt tax evasion because of the enhanced detection system.
Fifth, the ability to respond to international tax evasion was strengthened. It became possible to effectively identify complex tax evasion structures using overseas tax havens. In 2021, over 800 UK-related individuals involved in international tax evasion scandals, such as the Panama Papers, were identified and investigated.
Despite its outstanding performance, the "Connect" system faces several challenges.
First, there are issues regarding privacy and data protection. Because the system extensively collects and analyzes various personal data, concerns have been raised regarding personal information protection. In response, HMRC introduced strict data access controls and an independent oversight system.
Second is the possibility of false positives. The system sometimes misjudged normal activities as suspicious, leading to unnecessary investigations of innocent taxpayers. To address this, the accuracy of the algorithm is being continuously improved.
Third is the issue of technical countermeasures (Counter-AI). Some sophisticated tax evaders study the AI system's detection methods to develop new ways to bypass them. HMRC is continuously updating the system to respond to this.
HMRC is developing a next-generation system known as "Connect 2.0." This system is expected to include real-time data analysis, enhanced natural language processing, and more sophisticated network analysis functions. It also plans to expand data sharing and cooperation with international tax authorities to more effectively respond to cross-border tax evasion.
3. Monitoring Public Spending in Brazil
The Brazilian government is establishing an innovative Transparency Portal and an advanced AI-based Public Spending Observatory system to ensure transparency in public spending and prevent fraud. The establishment and operation of this system are led by the Comptroller General of the Union (CGU) of Brazil, with a multidisciplinary dedicated team consisting of data scientists, finance experts, audit experts, and IT professionals collaborating to ensure continuous improvement and operation of the system.
The system operates through five major components: data integration, anomaly detection, network analysis, text mining, and a risk scoring system.
Data integration gathers and stores all government contracts and spending information in one place in real time. It helps ensure transparency in the contracting process by linking various data such as public officials' personnel information, corporate ownership structures, and political connections. This can significantly reduce the risk of misconduct or unfair contract agreements during the process.
Anomaly detection algorithms use the latest AI and machine learning technologies to automatically identify unusual transactions. For example, it can detect suspicious situations such as costs being excessively high compared to similar contracts or a company repeatedly winning small contracts within a short period.
Network analysis technology visualizes and analyzes complex relationships between government contracts, public officials, and companies to thoroughly investigate fraud and corruption. By identifying direct or indirect relationships between officials and specific companies, it helps discover in advance the possibility of them engaging in unfair transactions or exercising influence.
Text mining technology automatically analyzes the contents of various government documents such as contracts, bidding papers, invoices, and audit reports to identify inconsistencies or inappropriate contract terms. Thanks to this technology, it is possible to find differences between actual contracts and the terms on paper, and convert them into standardized data for more accurate analysis.
The risk scoring system assigns a comprehensive risk score to all transactions and contracts based on various risk factors, allowing audit personnel to quickly prioritize areas that must be checked. This ensures that high risk contracts or transactions are automatically selected for more detailed auditing, and the specific degree of risk is managed by government department or business unit.
Technically, the system's processing speed and scalability have been maximized using high-performance data processing systems in a cloud environment and streaming methods for real-time data processing. Furthermore, a user friendly interface was created so that citizens can easily access and use it, and efficiency was increased by separately operating a dashboard used by government agencies and a public portal viewed by citizens.
Every month, more than 900,000 citizens, researchers, journalists, and public officials monitor government spending through this system. So far, this system has resulted in preventing or recovering improper spending amounting to billions of reais (BRL, R$). It has also contributed to raising Brazil's position in international transparency evaluations and serves as a good model for other countries as a founding member of the Open Government Partnership. The Brazilian government plans to continue developing the system's functions and more actively pursue international cooperation and technical exchange.
The Public Spending Observatory (Observatório da Despesa Pública, ODP) in Brazil is a platform designed to increase transparency in public finance and encourage citizen participation. Compared to other similar systems internationally, this system has several distinctive features.
Comparison with USAspending.gov: Brazil's ODP emphasizes accessibility to federal budget data and has an open structure that anyone can easily access without logging in. This facilitates active citizen participation, with an average of 900,000 visitors per month. In contrast, USAspending.gov structures federal government spending data and primarily provides analytical tools by budget item and program. Although there is no clear superiority in terms of user interface, the Brazil system's accessibility and encouragement of citizen participation are more emphasized.
Comparison with the UK's HMRC Connect: The UK's HMRC Connect is a powerful analytical tool specialized in detecting tax evasion and fraud through AI-driven data analysis. Its characteristic feature is analyzing complex data relationships by linking various sources such as bank records, real estate information, and social media data. In comparison, Brazil's ODP focuses on fraud prevention through active citizen participation, and its citizen monitoring function is relatively open and transparent. However, its analytical ability to identify complex relationships in data has not been clearly proven compared to HMRC Connect.
Comparison with South Korea's Digital Budget and Accounting System (dBrain): South Korea's dBrain provides integrated support for the entire process of fiscal management, from budget formulation to settlement, and emphasizes real-time management of the national treasury and performance analysis functions. The South Korean system combines the program budget system with accrual accounting to perform detailed fiscal management. In contrast, Brazil's ODP focuses more on fraud prevention and strengthening budget transparency, with the primary goals of transparently tracking public funds and monitoring the finances of local governments.
Comparison with India's PFMS (Public Financial Management System): India's PFMS manages fund flows between the central and local governments in real time and focuses on the Direct Benefit Transfer (DBT) function to beneficiaries. This supports Indian government programs overall and ensures transparency in fund execution. While Brazil's ODP focused on increasing transparency through the disclosure of real-time budget data, it relatively de-emphasizes real-time tracking of extensive government programs and beneficiary management functions like India's PFMS.
Kim Kyung-jin
Attorney · Former Member of the National Assembly · AI Policy Researcher
© 2026 Kim Kyung-jin. All rights reserved.