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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-for-fraud-detection-and-suspicious-transaction-monitoring/
课程评论：没有评论
课程名称：人工智能在欺诈检测与可疑交易监测中的应用 课程概述：金融行业面临着日益严重的欺诈交易和洗钱活动的检测和预防挑战。随着人工智能（AI）的快速发展，银行和金融机构正在利用AI驱动的解决方案来增强交易监控、检测可疑活动和遵守监管框架。本课程旨在提供对金融欺诈检测中AI应用的全面理解，涵盖关键概念、方法论以及来自全球领先银行的真实案例研究。 课程的开头部分介绍了欺诈检测的重要性以及银行中交易监测与可疑活动的必要性。接着，探讨了传统可疑活动检测的挑战，强调了常规欺诈检测系统的局限性和对AI驱动解决方案的需求。学习者将深入了解AI如何增强交易监控系统，提高准确性并减少误报。 课程的一个关键重点是关键风险指标（KRI）和交易中的警示信号，它们帮助金融机构识别潜在的欺诈活动。课程进一步深入了解“了解你的客户”（KYC）及反洗钱（AML）法规，并详细审视FATF、FinCEN和GDPR等监管框架。学习者将探索金融机构中AI驱动的KYC和AML解决方案，研究业界成功的实施案例。 此外，课程还涵盖金融交易监控中的关键自然语言处理技术、异常检测算法（监督学习与无监督学习）、以及用于欺诈检测的神经网络和AI模型。课程强调实际实施，并提供神经网络欺诈检测模型的部署指南以及AI模型的数据收集和预处理。 课程专门讨论银行中的交易类型及AI的角色，解释为什么贸易交易需要密切监控，以及AI如何增强监视能力。内容包括交易量大与AI解决方案、金融工具的复杂性与AI解决方案，及AI如何帮助检测新兴金融犯罪。 课程同样关注监管复杂性与AI解决方案、适应现有遗留系统的挑战，以及安全和数据隐私问题。随着AI技术的快速发展，银行在实施过程中面临各种挑战，课程讨论了资源限制及AI解决方案来应对这些问题。 课程中包含了深入的真实案例研究，展示了全球领先银行如汇丰银行、摩根大通、渣打银行、Danske银行、ING银行、DBS银行、印度工商银行、中国建设银行、三菱UFJ金融集团和恒生银行在AI驱动的欺诈检测解决方案中的成功案例。这些案例突显了这些金融机构如何成功部署AI以打击金融欺诈、洗钱及基于贸易的洗钱（TBML）。 通过本课程，学习者将对AI在欺诈检测和交易监控中的作用有深入理解，掌握实施AI驱动解决方案的知识。这门课程非常适合银行专业人员、合规官员、数据科学家和希望提升人工智能欺诈检测专业知识的AI爱好者。
The financial industry faces an ever-growing challenge in detecting and preventing fraudulent transactions and money laundering activities. With the rapid advancements in artificial intelligence (AI), banks and financial institutions are now leveraging AI-driven solutions to enhance transaction monitoring, detect suspicious activities, and comply with regulatory frameworks. This course, AI for Fraud Detection and Suspicious Transaction Monitoring in Banking, is designed to provide a comprehensive understanding of AI applications in financial fraud detection, covering key concepts, methodologies, and real-world case studies from leading global banks.The course begins with an Introduction, providing an overview of fraud detection and the Importance of Transaction Monitoring & Suspicious Activity in banking. It explores the Challenges in Traditional Suspicious Activity Detection, highlighting the limitations of conventional fraud detection systems and the need for AI-driven solutions. Learners will gain insights into How AI Enhances Transaction Monitoring Systems, improving accuracy and reducing false positives.A key focus of this course is on Key Risk Indicators (KRIs) and Red Flags in Transactions, which help financial institutions identify potential fraudulent activities. The course further delves into the Role of Know Your Customer (KYC) and Anti-Money Laundering (AML) Regulations, with a detailed examination of Regulatory Frameworks such as FATF, FinCEN, and GDPR. Learners will explore AI-Driven KYC and AML Solutions in Financial Institutions, studying successful implementations in the industry.The course also covers Key NLP Techniques in Financial Transaction Monitoring, Anomaly Detection Algorithms (Supervised vs. Unsupervised Learning), and Neural Networks and AI Models for Fraud Detection. Practical implementation is emphasized through an Implementation Guide for Deploying a Neural Network Fraud Detection Model and Data Collection & Preprocessing for AI Models.A specialized section on Types of Transactions in Banks and the Role of AI explains why trade transactions are closely monitored and how AI enhances surveillance. It examines the High Volume of Transactions & AI Solutions, The Complexity of Financial Instruments & AI Solutions, and how AI helps in Detecting Emerging Financial Crimes.The course also addresses Regulatory Complexity & AI Solutions, Adaptability to Existing Legacy Systems, and Security & Data Privacy Issues. With rapidly developing AI technologies, banks face challenges in implementation, and the course discusses Resource Restrictions & AI Solutions to navigate these issues.The course features in-depth Real-World Case Studies, showcasing AI-driven fraud detection solutions in leading global banks, including HSBC, JPMorgan Chase, Standard Chartered Bank, Danske Bank, ING Bank, DBS Bank, ICICI Bank, China Construction Bank (CCB), Mitsubishi UFJ Financial Group (MUFG), and Hang Seng Bank. These case studies highlight how these financial institutions successfully deploy AI in combating financial fraud, money laundering, and trade-based money laundering (TBML).By the end of the course, learners will gain a strong understanding of AI's role in fraud detection and transaction monitoring, equipping them with the knowledge to implement AI-driven solutions in banking and finance. The course is ideal for banking professionals, compliance officers, data scientists, and AI enthusiasts looking to enhance their expertise in AI-powered fraud detection.
