Ai For Fraud Detection And Suspicious Transaction Monitoring

AI in Banking Security - 10 Banks and 8 technology AI solutions covered.

What you'll learn

Understand the importance of transaction monitoring and suspicious activity detection in banking.

Explore how AI enhances transaction monitoring systems in financial institutions.

Risk Indicators, Regulations, and Compliance

Understand the role of Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations in transaction monitoring.

AI and Machine Learning in Fraud Detection

Get hands-on insights into the implementation of a neural network fraud detection model.

Transaction Types and AI Solutions

Study successful AI use cases from HSBC, JPMorgan Chase, Standard Chartered Bank, Danske Bank, ING Bank, DBS Bank, ICICI Bank, China Construction Bank (CCB) etc

Requirements

Basic Finance & Banking Knowledge

Description

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.

Overview

Section 1: Introduction to AI in Banking & Finance

Lecture 1 Introduction

Lecture 2 Importance of transaction monitoring & suspicious activity

Lecture 3 Challenges in traditional suspicious activity detection

Lecture 4 How Al Enhances Transaction Monitoring Systems in Banking & Finance

Section 2: Fundamentals of Suspicious Transaction Monitoring

Lecture 5 Key risk indicators (KRIs) and red flags in transactions

Lecture 6 Role of Know Your Customer (KYC) and Anti-Money Laundering (AML) Regulations

Lecture 7 Case Studies: AI-Driven KYC and AML Solutions in Financial Institutions

Lecture 8 Regulatory frameworks (FATF, FinCEN, GDPR, etc.)

Section 3: AI Technologies for Suspicious Activity Monitoring

Lecture 9 Key NLP Techniques in Financial Transaction Monitoring

Lecture 10 NLP Models Used in Banking Fraud Detection

Lecture 11 Anomaly detection algorithms (Supervised vs. Unsupervised Learning)

Lecture 12 Neural networks and Al models for fraud detection

Lecture 13 Implementation Guide for Deploying a Neural Network Fraud Detection Model

Lecture 14 Data Collection and Preprocessing for AI Models in Fraud Detection

Section 4: Types of Transactions in Banks & tracking

Lecture 15 Types of Transactions in Banks and the Role of Al

Lecture 16 Why Are Trade Transactions Tracked

Section 5: Transaction Monitoring & Different AI Solutions for Banks

Lecture 17 High Volume of Transactions & Al Solution

Lecture 18 The Complexity of Financial Instruments & Al Solution

Lecture 19 Developing Fraud Tactics and How Al Detects Emerging Financial Crimes

Lecture 20 Regulatory Complexity Jurisdiction Realities & Al Solution

Lecture 21 Adaptability to the existing legacy systems & Al Solution

Lecture 22 Security and Data Privacy Issues & Al Solution

Lecture 23 Rapidly developing technologies & Al Solution

Lecture 24 Restrictions on Resources & Al Solution

Section 6: Banks & Their AI solutions Discussed

Lecture 25 HSBC

Lecture 26 JPMorgan Chase

Lecture 27 Standard Chartered Bank

Lecture 28 Danske Bank

Lecture 29 ING Bank

Lecture 30 DBS Bank

Lecture 31 ICICI Bank (India)

Lecture 32 China Construction Bank (CCB)

Lecture 33 Mitsubishi UFJ Financial Group (MUFG)

Lecture 34 Hang Seng Bank

Banking and Finance Professionals,Fraud Analysts & Risk Managers,Compliance Officers & AML/KYC Specialists,Banking Executives & Decision-Makers,Data Scientists & AI Practitioners,Machine Learning Engineers & AI Developers,Data Analysts & Financial Data Scientists,Cybersecurity Analysts,Financial Crime Investigators,FinTech Professionals & AI Consultants,IT Architects & System Integrators,Finance, AI, and Cybersecurity Students,Researchers in Financial AI