本研究旨在點出我國金融機構執行洗錢防制「風險基礎方法」之結構性困境，因近年合規成本呈指數級增長，機構為避罰採取零容忍態度，導向海量防禦性申報與資源錯置，陷入「高成本、低實質效益」之失衡，在意識到此問題後，透過結合犯罪學理論反思如何修正洗錢防制「風險基礎方法」，將形式法遵導向實質犯罪預防，具體引入理性選擇、情境預防與日常活動理論解構洗錢現象，進而才能更精準有效預防洗錢，並首創「偏資金收付行業」與「全對價交易行業」雙控模型：銀行業掌握金流卻缺契約，保險與證券業掌握契約卻無從知曉資金源頭，洗錢者精準利用此資訊斷層，致使機構淪為失明的守護者。
隨著執法加嚴完成傳統帳戶之「目標硬化」，犯罪隨即產生轉移與氣球效應，流向空殼公司之法人帳戶、高匿名性之虛擬資產與第三方支付，並夾雜理專或行員勾結之內部人威脅。比較美、英、中等國法制，皆已轉向情資共享與科技應用；反觀我國洗錢防制法第17條嚴格之洩密禁止條款，原意保護偵查，反倒成為阻礙機構橫向預警之緊箍咒。
為突破資訊孤島，本研究提出三項創新策略。一為建立「個人洗錢風險報告制度」，借鏡信用報告模式，在授權下整合跨機構行為特徵，產出去識別化風險分數以精準執行審查；二為設立「公私部門交流平台」，建立雙向情資交流機制與安全港保護，實現橫向共享與縱向威脅發布；三為引入「聯合學習」技術，在原始數據不出本地前提下分享加密參數進行協同訓練，克服數據稀疏性並大幅降低誤報率。
落實上述策略必須推動法制根本調整。本研究提出三項修法建議：增訂洗錢防制法第8條第4項，確立風險報告之法定效益，允許低風險者簡化審查；增訂第17條之1，建立情資共享之「安全港」法律豁免，排除照會他行之洩密刑責；修正第27條，由義務機構共同提撥設置「財團法人洗錢防制基金」，專責平台維運與聯合學習統籌。唯有透過法制解綁與科技賦能，將體系轉型為智慧聯防的主動防禦網絡，方能引導風險基礎方法回歸實質有效正軌。

This study aims to highlight the structural dilemma faced by domestic financial institutions in implementing the "Risk-Based Approach" (RBA) to Anti-Money Laundering (AML). Exponential growth in compliance costs in recent years has driven institutions to adopt a zero-tolerance stance to avoid penalties, resulting in massive defensive reporting, misallocated resources, and an imbalance of "high cost, low substantial benefit." Recognizing this issue, this paper integrates criminological theories—specifically rational choice, situational prevention, and routine activity theories—to rethink and reform the RBA, shifting focus from formal compliance to substantive crime prevention to deconstruct money laundering dynamics for more precise and effective prevention. Furthermore, it pioneers a dual-control model distinguishing between "cash-flow-oriented" and "full-consideration transaction" sectors: banks possess cash flows but lack underlying contracts, while insurance and securities firms possess contracts but lack visibility into funding sources. Money launderers precisely exploit this information gap, turning institutions into blind guardians.
Following target hardening on traditional individual accounts, criminal activities rapidly shifted, creating a balloon effect toward corporate accounts using shell companies, highly anonymous virtual assets, and third-party payment platforms, compounded by insider threats from corrupt bank employees. Comparative legal analysis shows that the US, UK, and China have shifted toward information sharing and technology adoption. Conversely, Article 17 of Taiwan’s Money Laundering Control Act imposes strict anti-tipping-off rules. Originally intended to protect criminal investigations, it now acts as a legal straightjacket that prevents cross-institutional horizontal warnings.
To break through these information silos, this study proposes three innovative strategies. First, a Personal AML Risk Reporting System should be established—modeled after credit reporting—to aggregate cross-institutional behavioral traits under customer consent and generate anonymized risk scores for targeted due diligence. Second, a Public-Private Information Exchange Platform backed by a legal "safe harbor" should facilitate horizontal information sharing among institutions and vertical threat intelligence distribution from law enforcement. Third, Federated Learning technology should be introduced to allow collaborative AI model training without raw data leaving local databases, overcoming data sparsity and drastically reducing false positives.
Implementing these innovations requires fundamental legal restructuring. This study proposes three amendments to the Money Laundering Control Act: adding Article 8, Paragraph 4 to authorize risk reports and allow simplified due diligence for low-risk clients; adding Article 17-1 to establish a "safe harbor" clause exempting institutions from tipping-off liability during cross-bank inquiries; and amending Article 27 to establish a dedicated Anti-Money Laundering Foundation funded by reporting entities to manage the platform and federated learning operations. Only through regulatory unbinding and technological empowerment can Taiwan transform its AML framework from passive, isolated compliance into a smart, collaborative defense network, restoring the Risk-Based Approach to true effectiveness.

中文文獻（按姓氏筆畫排序）
(一) 書籍
1. 王任翔（2019），洗錢防制法 銀行業實務挑戰（2版）。元照。
2. 林山田、林東茂、林燦璋、賴擁連（2020），犯罪學（6版）。三民。
3. 蔣念祖（2021），洗錢防制國際評鑑與風險治理。元照。
4. 蔡德輝、楊士隆（2019），犯罪學（8版）。五南。
(二) 期刊論文
1. 王志誠（2017），〈洗錢防制法之發展趨勢─金融機構執行洗錢防制之實務問題〉，月旦法學雜誌，第267期，頁5-18。
2. 吳盈德（2017），〈創新金融科技與洗錢防制趨勢〉，月旦法學雜誌，第267期，頁26。
3. 吳盈德（2018），〈公司法修正與洗錢防制遵循〉，萬國法律，第222期，頁2-12。
4. 李秉錡（2018），〈鳥瞰洗錢交易類型 (上)〉，檢察會訊，第116期，頁18-19。
5. 李秉錡（2026），〈2024年洗錢防制法中刑事責任之實務問題與展望〉，全國律師雜誌，2026年2月刊，頁5-17。
6. 林士淳（2020），〈洗錢防制作為與個人隱私保護之調和─以歐盟立法例為中心（上）〉，檢協會訊，第122期，頁15-24。
7. 林士淳（2020），〈洗錢防制作為與個人隱私保護之調和─以歐盟立法例為中心（下）〉，檢協會訊，第123期，頁2-11。
8. 林佑翠（2020），〈全球最永續銀行違反反洗錢與反恐怖主義融資法2,300萬次的代價-西太平洋銀行（Westpac）案例〉，華人經濟研究，第18卷第1期，頁11-20。
9. 林志潔（2016），〈兆豐案天價罰款的啟示—美國反洗錢法的重點與金融業應有的作為〉，月旦法學雜誌，第259期，頁34-48。
10. 林佳儀（2024），〈歐洲洗錢防制新發展：聚焦交易監控與個資保護之兩難（上）〉，集保電子雙月刊，第275期。
11. 林佳儀（2024），〈歐洲洗錢防制新發展：聚焦交易監控與個資保護之兩難（下）〉，集保電子雙月刊，第276期。
12. 林國彬（2018），〈我國近年來投資型保險商品之發展、監理與爭議問題研究(上)〉，月旦法學雜誌，第281期，頁114-129。
13. 林琦珍、黃劭彥、許美滿（2020），〈銀行業遵循洗錢防制法之自動化查核運用〉，月旦會計實務研究，第32期，頁40-51。
14. 林瑞彬、劉家全（2017），〈洗錢防制新法下的法令遵循重點〉，月旦刑事法評論，第4期，頁130-136。
15. 林盟翔（2017），〈數位通貨與普惠金融之監理變革─兼論洗錢防制之因應策略〉，月旦法學雜誌，第267期，頁30-75。
16. 邵之雋、李怡萱（2023），〈人壽保險公司所受洗錢及資恐風險與相應抵減風險措施研究〉，交大法學評論，第13期，頁123-160。
17. 國家發展委員會產業發展處（2021），〈AI 聯合學習大聯盟啟動〉，台灣經濟論衡，第19卷第1期，頁87-89。
18. 游彥城、林志潔（2024），〈面對金融科技浪潮的來襲──犯罪防制觀點〉，交大法學評論，第14期，頁99-132。
19. 程法彰（2018），〈洗錢防制與個人資料保護的兩難〉，全國律師，第22卷第11期，頁65-69。
20. 黃天牧、邵之雋（2019），〈我國保險業暨保險輔助人間防制洗錢暨打擊資恐監理之研究〉，交大法學評論，第4期，頁16-27。
21. 黃綺聿、陳俊元（2019），〈我國保險業洗錢防制法制之研究〉，壽險管理期刊，第32卷第4期，頁20-21。
22. 楊岳平（2022），〈金融科技時代下金融資料共享法制之發展與限制－評「金融機構間資料共享指引」〉，台灣法律，第17期，頁99-114。
23. 廖世昌、郭姿君（2017），〈保險業防制洗錢及打擊資恐之法令遵循〉，月旦法學雜誌，第267期，頁82-84。
24. 蔡佩玲（2017），〈洗錢防制法新法修正重點解析〉，檢察新論，第21期，頁46-95。
25. 蔡佩玲（2017），〈錢的秩序與遊戲─洗錢防制新法解析〉，月旦刑事法評論，第4期，頁109-116。
26. 蔡昌憲、彭冠蓉（2021），〈開放銀行之管制政策研究─以歐盟與英國的經驗為中心〉，月旦法學雜誌，第313期，頁76-96。
27. 蔡信華（2019），〈保險業客戶審查義務及洗錢防制之法令遵循－兼論OIU國際客戶審查〉，國立中正大學法學集刊，第62期，頁109-170。
28. 蔡信華（2019），〈保險業客戶審查義務及洗錢防制之法令遵循—兼論OIU國際客戶審查〉，國立中正大學法學集刊，第62期，頁15-17。
29. 蔡鐘慶（2019），〈公司洗錢防制之研究─以公司申報董監事及大股東資料義務為中心〉，財產法暨經濟法，第56期，頁53-87。
30. 蔣念祖（2018），〈洗防、資恐立法進程與國際評鑑〉，萬國法律，第222期，頁34-47。
31. 蔣念祖（2018），〈洗錢防制評鑑與實質受益人範圍之分析〉，台灣法學雜誌，第343期，頁14-27。
32. 鄭旭高（2020），〈洗錢防制、監理科技與金融科技〉，月旦會計實務研究，第32期，頁52-56。
33. 戴凡芹（2025），〈日本永旺銀行反洗錢案例的借鏡—反思臺灣如何應對APG評鑑(上) 〉，法務通訊，3283期，頁3-6。
34. 戴凡芹（2025），〈日本永旺銀行反洗錢案例的借鏡—反思臺灣如何應對APG評鑑(下) 〉，法務通訊，3284期，頁3-6。
35. 戴凡芹（2025），〈從FATF評鑑看日本實質受益人制度演進：對我國的政策啟示〉，萬國法律，263期，頁85-94。
36. 戴凡芹（2026），〈洗錢防制的關鍵環節～公司實質受益人揭露之法制與政策意涵〉，法務通訊，3294期，頁3-6。
37. 魏至潔（2018），〈國際洗錢防制法規趨勢─以美國愛國者法案為例〉，清流雙月刊，第28期，頁28-31。
38. 蘇佩鈺（2022），〈公私部門資訊共享與偵查不公開之界限〉，東海大學法學研究，第63期，頁151-193。
(三) 政府出版品與公告
1. 中國人民銀行（2021），金融機構反洗錢和反恐怖融資監督管理辦法（中國人民銀行令〔2021〕第3號）。
2. 中國人民銀行（2025），金融機構洗錢和恐怖融資風險自評估指引（中國人民銀行令〔2025〕第11號）。
3. 中國人民銀行（2025），金融機構客戶盡職調查和客戶身份資料及交易記錄保存管理辦法（中國人民銀行令〔2025〕第12號）。
4. 中華人民共和國國務院辦公廳（2024），關於加強打擊治理洗錢違法犯罪工作的意見。
5. 法務部調查局（2025），2024 年洗錢防制工作年報。
6. 金融監督管理委員會（2024），金融業運用人工智慧 (AI) 指引。
外文文獻
(一) 書籍
1. Clarke, Ronald V. & Marcus Felson, Routine Activity Theory and Its Application to Crime Prevention, in Routine Activity and Rational Choice 1 (Ronald V. Clarke & Marcus Felson eds., 1993).
2. Clarke, Ronald V., Situational Crime Prevention: Successful Case Studies (3rd ed. 2017).
3. Cornish, Derek B. & Ronald V. Clarke eds., The Reasoning Criminal: Rational Choice Perspectives on Offending (1986).
4. Dolan, Catherine & Jennifer Bergin, Public-Private Partnerships for Financial Information Sharing (2025).
5. Fontaine, Claire et al., Communication-Efficient Federated Learning for Real-Time Anti-Money-Laundering Monitoring (Dec. 5, 2025) (unpublished manuscript).
6. Gilmour, Nicholas John, Improving the Prevention of Money Laundering in the United Kingdom—A Situational Crime Prevention Approach (2013) (unpublished Ph.D. dissertation, University of Portsmouth).
7. Guerette, R. T. & A. Aziani, The Displacement and Convergence of Transnational Crime Flows, in The Evolution of Illicit Flows 50 (E. U. Savona et al. eds., 2022).
8. Levi, Michael & Russell G. Smith, Fraud Vulnerabilities and the Global Financial Crisis (2011).
9. McMahan, Brendan et al., Communication-Efficient Learning of Deep Networks from Decentralized Data, in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (2017).
10. McGough, Caitlin M., Evaluating the Impact of AML/CFT Regulations: An Economic Approach (2016) (unpublished Honors thesis, Duke University).
11. Madinger, John, Money Laundering: A Guide for Criminal Investigators (3rd ed. 2012).
12. McGough, Caitlin M., Evaluating the Impact of AML/CFT Regulations: An Economic Approach (2016) (unpublished Honors thesis, Duke University).
13. Schott, Paul Allan, Reference Guide to Anti-Money Laundering and Combating the Financing of Terrorism (2nd ed. 2006).
(二) 期刊論文
1. Aljunaid, Saif Khalifa et al., Secure and Transparent Banking: Explainable AI-Driven Federated Learning Model for Financial Fraud Detection, 18 Journal of Risk and Financial Management 179 (2025).
2. Connelly, Matthias, Can AI Fix Anti-Money Laundering? The Case for Federated Intelligence in Financial Crime Prevention, 4 Stanford Journal of Intellectual Property and Law 62 (2026).
3. Dehghanniri, Hashem & Hervé Borrion, Crime Scripting: A Systematic Review, 18 European Journal of Criminology 504 (2021).
4. Friesendorf, Cornelius, Squeezing the Balloon? United States Air Interdiction and the Restructuring of the South American Drug Industry in the 1990s, 44 Crime, Law and Social Change 35 (2005).
5. Gottschalk, Petter, Modeling the Theoretical Structure of Deviant Convenience in White-Collar Crime, 42 Deviant Behavior 1345 (2021).
6. Isa, Y. Mat et al., Routine Activity Theory and the Dynamics of Money Laundering: Reassessing Emerging Threats in Banking Operations, 28 Journal of Money Laundering Control 764 (2025).
7. Kleemans, Edward R. et al., Organized Crime, Situational Crime Prevention and Routine Activity Theory, 15 Trends in Organized Crime 87 (2012).
8. Levi, Michael, Evaluating the Control of Money Laundering and Its Underlying Offences: The Search for Meaningful Data, 15 Asian Journal of Criminology 301 (2020).
9. Levi, Michael & Melvin Soudijn, Understanding the Laundering of Organized Crime Money, 49 Crime and Justice 579 (2020).
10. Mawardi, Rizal et al., Digital Financial Compliance Challenges: Applying Routine Activity Theory to Online Gambling Networks Analysis, 10 Emerging Science Journal 315 (2026).
11. Ogunsola, O. et al., Standardizing Compliance Practices across AML, ESG, and Transaction Monitoring for Financial Institutions, Journal of Frontiers in Multidisciplinary Research 75 (2024).
12. Sehat, Rahayu Mohd & Noor Faiza M. Jaafar, The ESG-AML Convergence Challenge: A New Financial Criminology Perspective for Malaysia, 9 International Journal of Research and Innovation in Social Science 559 (2025).
13. Tiwari, M. et al., Factors Influencing the Choice of Technique to Launder Funds: The APPT Framework, 1 Journal of Economic Criminology 100006 (2023).
14. Unger, Brigitte, Money Laundering: The Unintended Consequences of the War on Crime, 38 Journal of Economic Perspectives 115 (2024).
15. Upadrista, Venkatesh et al., Anti-Money Laundering (AML) Detection Platform Leveraging Federated Learning, 10 Journal of Theoretical and Computational Science 1 (2024).
16. Walters, Glenn D., The Decision to Commit Crime: Rational or Nonrational?, 16 Criminology, Criminal Justice, Law & Society 1 (2015).
17. Windle, James & Graham Farrell, Popping the Balloon Effect: Assessing Drug Law Enforcement in Terms of Displacement, Diffusion, and the Containment Hypothesis, 47 Substance Use & Misuse 868 (2012).
18. Yaramolu, Leela Sri Kalyan Gowtham, Privacy-driven Federated AI in Financial Fraud Detection and Risk Scoring, 15 World Journal of Advanced Engineering Technology and Sciences 41 (2025).
(三) 政府出版品與公告
1. APG, Anti-Money Laundering and Counter-Terrorist Financing Measures – Chinese Taipei, Third Round Mutual Evaluation Report (2019).
2. Bank for International Settlements, Project Aurora: The Power of Data, Technology and Collaboration to Combat Money Laundering (2023).
3. College of Policing, What Is Situational Crime Prevention? (2026).
4. Europol, EFIPPP 2024 Annual Report (2025).
5. FATF, Asset Recovery Guidance and Best Practices (2025).
6. FATF, Guidance for a Risk-Based Approach: Life Insurance Sector (2018).
7. FATF, Guidance on Digital Identity (2020).
8. FATF, Guidance on Financial Inclusion and Anti-Money Laundering and Terrorist Financing Measures (2025).
9. FATF, Guidance on Private Sector Information Sharing (2017).
10. FATF, International Standards on Combating Money Laundering and the Financing of Terrorism and Proliferation (2025).
11. FATF, Learning and Development Forum Meeting Insights (2026).
12. FATF, Professional Money Laundering (2018).
13. FATF, Targeted Report on Stablecoins and Unhosted Wallets: Peer-to-Peer Transactions (2026).
14. FATF, Targeted Update on Implementation of the FATF Standards on Virtual Assets/VASPs (2025).
15. FATF, Updated Guidance for a Risk-Based Approach: Virtual Assets and Virtual Asset Service Providers (2021).
16. FATF, Webinar: Promoting Financial Inclusion Through a Risk-Based Approach to AML/CFT and Guidance Updates (2025).
17. FCA, Proceeds of fraud - Detecting and preventing money mules (2023).
18. FCA, Research Note: Open banking and open finance in the UK (2025).
19. FinCEN, 314(b) Infographic: Participation and Reporting (2020).
20. FinCEN, FinCEN Exchange Brings Together Public and Private Stakeholders to Discuss Bank Secrecy Act Suspicious Activity Reporting Statistics (2021).
21. FinCEN, FinCEN Exchange in New York City Focuses on Virtual Currency (2021).
22. FinCEN, Insurance Industry Suspicious Activity Reporting (2010).
23. FinCEN, Interagency Guidance on Sharing Suspicious Activity Reports with Head Offices and Controlling Companies (2006).
24. FinCEN, Interagency Statement on Sharing Bank Secrecy Act Resources (2018).
25. FinCEN, Section 314(b) Fact Sheet (2020).
26. FinCEN, Survey of the Costs of AML/CFT Compliance (2026).
27. Global Reporting Initiative, GRI 2: General Disclosures 2021 (2021).
28. H.M. Treasury and Home Office, Action Plan for Anti-Money Laundering and Counter-Terrorist Finance (2016).
29. H.M. Treasury and Home Office, Economic Crime Plan 2019 to 2022 (2019).
30. McMahan, Brendan & Daniel Ramage, Federated Learning: Collaborative Machine Learning without Centralized Training Data, Google Research Blog (Apr. 6, 2017).
31. McKinsey & Co., How Agentic AI Can Change the Way Banks Fight Financial Crime (2026).
32. National Crime Agency, Improving the UK’s Response to Economic Crime (2026).
33. National Crime Agency, Required Notification Under § 339ZC of Proceeds of Crime Act 2002 (2017).
34. Principles for Responsible Investment, Principles for Responsible Investment (2006).
35. Pokorny, L., Longitudinal Analysis of Public Benefits Fraud Evolution in Minnesota's Somali Community: A Time-Series Examination of Criminal Adaptation, Policy Interventions, and Detection Patterns (2018–2025) (ICL Institute, Dep't of Forensic Scis., 2025).
36. PwC UK, EMEA AML Survey 2024 (2024).
37. Radončić, Alida, Artificial Intelligence in Anti-Money Laundering, IACA Research Paper No. 1 (2026).
38. Terrorism, Transnational Crime and Corruption Center, Internationalizing the Fight Against Hubs of Illicit Trade and Criminalized Markets (White Paper, George Mason University, 2023).
39. U.S. Department of the Treasury, 2024 National Strategy for Combating Terrorist and Other Illicit Financing (2024).
40. UN Global Compact, Who Cares Wins: Connecting Financial Markets to a Changing World (2004).