AI for Fraud Detection and Suspicious Transaction Monitoring

Name: AI for Fraud Detection and Suspicious Transaction Monitoring
Rating: 4.0 (98 reviews)

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

Created byEdcorner Learning

Last updated 5/2026

English

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

Course content

6 sections • 34 lectures • 4h 55m total length

Introduction9:25
Explore how artificial intelligence transforms financial services by boosting fraud detection, anti-money laundering, risk management, customer service, and automation, with applications in credit scoring, investment management, regtech, and compliance.
Importance of transaction monitoring & suspicious activity10:42
Leverage ai-driven transaction monitoring to detect suspicious activity in real time, reduce false positives, and support AML and CTF reporting across cross-border and crypto transactions.
Challenges in traditional suspicious activity detection8:43
Explore how traditional rule based suspicious activity detection in banking struggles with false positives and lacks time monitoring, while AI powered transaction monitoring enhances detection of cross-border and insider fraud.
How Al Enhances Transaction Monitoring Systems in Banking & Finance11:46

Key NLP Techniques in Financial Transaction Monitoring9:16
Explore how natural language processing analyzes unstructured data to detect fraud, money laundering, and regulatory risks in financial transaction monitoring, using techniques like named entity recognition and OCR.
NLP Models Used in Banking Fraud Detection3:04
Anomaly detection algorithms (Supervised vs. Unsupervised Learning)8:39
Neural networks and Al models for fraud detection7:26
Leverage neural networks and AI models for real-time fraud detection in banking, using architectures like LSTM, CNN, RNNs, autoencoders, and graph neural networks.
Implementation Guide for Deploying a Neural Network Fraud Detection Model2:42
Data Collection and Preprocessing for AI Models in Fraud Detection3:45

Types of Transactions in Banks and the Role of Al8:49
Discover how AI transforms banking transaction monitoring across cash, online, mobile, card, wire transfers, and loans, boosting fraud detection, AML compliance, and real-time risk scoring.
Why Are Trade Transactions Tracked8:51
Explore how trade transactions are tracked to prevent trade based money laundering, fraud, and sanctions violations using AI‑driven monitoring and anomaly detection across documents and invoices.

High Volume of Transactions & Al Solution16:28
Harness Feedzai's ai powered fraud detection to monitor millions of transactions in real time, reducing false positives with adaptive machine learning, anomaly detection, and risk scoring.
The Complexity of Financial Instruments & Al Solution9:03
Investigate how derivatives, structured products, and forex introduce fraud and money laundering risks, and see how Nice Actimize uses AI, ML, DL, and big data analytics to detect them.
Developing Fraud Tactics and How Al Detects Emerging Financial Crimes9:07
Explore ai-powered fraud detection with Darktrace, learning transaction patterns to detect anomalies in real time while addressing synthetic identity fraud, deepfakes, and social engineering.
Regulatory Complexity Jurisdiction Realities & Al Solution8:29
Navigate regulatory complexity across jurisdictions with AI-powered real-time fraud detection, risk scoring, and automated compliance reporting using Fico Falcon to prevent fraud while meeting AML and privacy rules.
Adaptability to the existing legacy systems & Al Solution9:12
Palantir Foundry integrates with legacy banking systems to enable real-time suspicious activity monitoring using AI, graph analytics, and risk scoring, while automating regulatory reporting and maintaining compliance.
Security and Data Privacy Issues & Al Solution9:31
Assess security and data privacy risks in AI-driven fraud detection for banking, highlighting real-time transaction monitoring, anomaly detection, and Theta Ray's privacy-preserving, risk-based approach.
Rapidly developing technologies & Al Solution8:51
Explore how real-time blockchain monitoring with Chainalysis helps banks detect illicit crypto activity, assess risk, and comply with AML and FATF guidelines.
Restrictions on Resources & Al Solution9:23
Explore how banks overcome resource constraints with AI powered identity verification, biometric authentication, and real time transaction monitoring from Jumio, boosting KYC/AML compliance and fraud detection.

HSBC9:04
Leverage Quantexa and Google Cloud AI to power real-time AML and fraud detection at HSBC, using graph analytics, entity resolution, contextual AI to reduce false positives and automate SAR filing.
JPMorgan Chase8:58
JPMorgan Chase employs AI-powered fraud detection with Ayasdi and SAS to monitor real-time transactions, reduce false positives, and uncover fraud networks using topological data analysis.
Standard Chartered Bank8:22
Explore AI-powered monitoring at Standard Chartered Bank that detects trade-based money laundering using Silent Eight and IBM Watson to flag high-risk transactions in real time.
Danske Bank8:22
Danske Bank's case study shows AI powered real time fraud prevention with Feedzai across mobile, online, and card channels, reducing false positives and blocking high risk transactions in milliseconds.
ING Bank9:14
Deploy real-time AML monitoring with Nice Actimize and Microsoft Azure AI at ING Bank. Reduce false positives and scale global compliance through AI-driven fraud detection.
DBS Bank9:50
Leverage ai powered real time aml and fraud detection with SAS and IBM Watson to monitor transactions, reduce false positives, and prioritize high risk cases for compliance teams.
ICICI Bank (India)8:10
Explore how ICICI Bank uses Feedzai's AI-powered real-time transaction monitoring across mobile, internet, and card payments to detect anomalies, reduce false positives, and prevent fraud.
China Construction Bank (CCB)8:19
Explore how China Construction Bank deploys AI driven trade surveillance with Silent Eight and Ficco to detect trade based money laundering, automate anomaly detection, and strengthen regulatory compliance.
Mitsubishi UFJ Financial Group (MUFG)9:40
MUFG strengthens real-time market surveillance and transaction monitoring with Nice Actimize AI, Microsoft Azure AI, and Hyperledger Fabric, detecting insider trading and market manipulation while ensuring regulatory compliance.
Hang Seng Bank8:32

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.

Who this course is for:

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

AI for Fraud Detection and Suspicious Transaction Monitoring

What you'll learn

Explore related topics

Course content

Introduction to AI in Banking & Finance4 lectures • 41min

Fundamentals of Suspicious Transaction Monitoring4 lectures • 34min

AI Technologies for Suspicious Activity Monitoring6 lectures • 35min

Types of Transactions in Banks & tracking2 lectures • 18min

Transaction Monitoring & Different AI Solutions for Banks8 lectures • 1hr 20min

Banks & Their AI solutions Discussed10 lectures • 1hr 29min

Requirements

Description

Who this course is for: