AI-Driven Fraud Detection in Banking

Available Dates

Dec-30-2024

London
Jan-06-2025

London
Feb-03-2025

London
Mar-03-2025

London
Apr-07-2025

London
May-05-2025

London
June-02-2025

London
June-30-2025

London
July-07-2025

London
Aug-04-2025

London
Sep-01-2025

London
Sep-29-2025

London
Oct-06-2025

London
Nov-03-2025

London
Dec-01-2025

London
Dec-29-2025

London

Dates in Other Venues

Dec-09-2024

Dubai
Dec-09-2024

Barcelona
Dec-16-2024

Kuala Lumpur
Dec-16-2024

Istanbul
Dec-23-2024

Singapore
Dec-30-2024

Amsterdam
Dec-30-2024

Paris
Jan-06-2025

Amsterdam
Jan-13-2025

Barcelona
Jan-13-2025

Dubai
Jan-20-2025

Kuala Lumpur
Jan-20-2025

Istanbul
Jan-27-2025

Singapore
Jan-27-2025

Paris
Feb-03-2025

Amsterdam
Feb-10-2025

Barcelona
Feb-10-2025

Dubai
Feb-17-2025

Istanbul
Feb-17-2025

Kuala Lumpur
Feb-24-2025

Singapore
Feb-24-2025

Paris
Mar-03-2025

Amsterdam
Mar-10-2025

Dubai
Mar-10-2025

Barcelona
Mar-17-2025

Istanbul
Mar-17-2025

Kuala Lumpur
Mar-24-2025

Singapore
Mar-24-2025

Paris
Apr-07-2025

Amsterdam
Apr-14-2025

Barcelona
Apr-14-2025

Dubai
Apr-21-2025

Kuala Lumpur
Apr-21-2025

Istanbul
Apr-28-2025

Singapore
Apr-28-2025

Paris
May-05-2025

Amsterdam
May-12-2025

Barcelona
May-12-2025

Dubai
May-19-2025

Istanbul
May-19-2025

Kuala Lumpur
May-26-2025

Paris
May-26-2025

Singapore
June-02-2025

Amsterdam
June-09-2025

Barcelona
June-09-2025

Dubai
June-16-2025

Istanbul
June-16-2025

Kuala Lumpur
June-23-2025

Singapore
June-30-2025

Paris
June-30-2025

Amsterdam
July-07-2025

Amsterdam
July-14-2025

Dubai
July-14-2025

Barcelona
July-21-2025

Istanbul
July-21-2025

Kuala Lumpur
July-28-2025

Singapore
July-28-2025

Paris
Aug-04-2025

Amsterdam
Aug-11-2025

Dubai
Aug-11-2025

Barcelona
Aug-18-2025

Istanbul
Aug-18-2025

Kuala Lumpur
Aug-25-2025

Paris
Aug-25-2025

Singapore
Sep-01-2025

Amsterdam
Sep-08-2025

Dubai
Sep-08-2025

Barcelona
Sep-15-2025

Istanbul
Sep-15-2025

Kuala Lumpur
Sep-22-2025

Singapore
Sep-29-2025

Paris
Sep-29-2025

Amsterdam
Oct-06-2025

Amsterdam
Oct-13-2025

Dubai
Oct-13-2025

Barcelona
Oct-20-2025

Istanbul
Oct-20-2025

Kuala Lumpur
Oct-27-2025

Singapore
Oct-27-2025

Paris
Nov-03-2025

Amsterdam
Nov-10-2025

Barcelona
Nov-10-2025

Dubai
Nov-17-2025

Istanbul
Nov-17-2025

Kuala Lumpur
Nov-24-2025

Singapore
Nov-24-2025

Paris
Dec-01-2025

Amsterdam
Dec-15-2025

Dubai
Dec-15-2025

Istanbul
Dec-15-2025

Kuala Lumpur
Dec-15-2025

Barcelona
Dec-22-2025

Singapore
Dec-29-2025

Amsterdam
Dec-29-2025

Paris

Course Details

Fraudulent activities in the banking sector pose significant challenges, threatening financial stability and customer trust. Artificial intelligence has emerged as a powerful tool in combating these threats, offering advanced techniques for identifying and mitigating fraud.

AI-driven fraud detection systems leverage machine learning and data analysis to recognize complex patterns and anomalies that traditional methods may overlook. By continuously analyzing transactional data and adapting to new fraudulent techniques, AI enhances the speed and accuracy of fraud detection.

The "AI-Driven Fraud Detection in Banking" course provides a comprehensive overview of these technologies, equipping participants with the knowledge and skills needed to implement AI solutions in fraud prevention. Over five days, the course covers AI fundamentals, data handling, machine learning models, and generative AI applications in fraud detection.

Through hands-on workshops and real-world case studies, participants will learn to build and deploy AI systems that comply with financial regulations and effectively protect against fraud in banking environments.

Upon completion of this course, attendees will be able to:

Gain a thorough understanding of the scope, impact, and types of fraudulent activities prevalent in the financial sector.
Understand the fundamental principles of AI and machine learning as applied to fraud detection.
Identify fraudulent patterns in the banking sector using AI and machine learning.
Utilize both supervised and unsupervised learning techniques to identify fraudulent activities.
Implement advanced fraud detection models using generative AI to analyze network and log data.
Deploy effective AI solutions to combat fraud within banking systems while ensuring compliance with financial regulations.

This course is tailored for professionals and learners who seek to understand and implement AI-driven strategies for detecting and preventing fraud within the banking and financial sectors:

Data Scientists and Analysts in the Financial Sector
Risk managers and fraud analysts
IT Security Professionals in Banking
Compliance Officers and Regulatory Professionals
Financial Technology Developers
Students and academics specializing in financial security

Our courses in London are held at the LPC office located at:

LPC Oxford Street Offices: 25 N Row, London W1K 6DJ

London Head Office: 47 - 49 Park Royal Road, London NW10 7LQ

Once you register, we will subsequently send you the course details, including the location, trainer, and other logistical information.

Pay Attention, Please! The course location at our offices is subject to availability. Should our office be unavailable, we will secure an alternative nearby venue and promptly inform you of the change. The exact time and location will be confirmed one week prior to the course commencement.

Course Outline

5 days course

Day 1

Day 2

Day 3

Day 4

Day 5

Introduction to AI in Fraud Detection

Overview of Fraud in the Financial Sector: Understanding the scope and impact of fraudulent activities in banking.
AI and Machine Learning Fundamentals: Introduction to the technologies driving modern fraud detection systems.
Role of AI in Identifying Fraud Patterns: How AI is used to enhance detection capabilities and speed in identifying fraud.
Tools and Technologies: Review of software and tools commonly used in AI-driven fraud detection.
Case Studies: Real-world examples of AI successfully preventing fraud in major financial institutions.

Data Handling & Analysis

Python for Financial Fraud Detection: Basics of Python programming focused on applications in fraud detection.
Exploratory Data Analysis (EDA): Techniques using Python to visualize and analyze transactional data for suspicious patterns.
Data Management Best Practices: Ensuring data integrity, security, and privacy when handling financial data.
Workshop: Hands-on session where participants use Python to perform EDA on anonymized banking datasets.
Discussion: Overcoming challenges in data analysis for fraud detection.

Machine learning concepts & fraud detection

Introduction to Machine Learning Models: Overview of supervised and unsupervised learning models used in fraud detection.
Unsupervised Learning Techniques: A deep dive into clustering and anomaly detection methods specifically used for identifying unusual patterns indicative of fraud.
Hands-On Workshop: Building and applying unsupervised learning models to detect fraud in financial datasets.
Model Evaluation and Validation: Techniques to assess the effectiveness of fraud detection models.
Q&A session: discussing practical challenges and solutions in machine learning for fraud detection.

Advanced Fraud Detection Using Generative AI

Generative AI in Fraud Detection: Introduction to generative models and their applications in understanding complex fraud mechanisms.
Analyzing Network and Log Data: Using AI to monitor and analyze vast amounts of network data for signs of fraudulent activity.
Hands-On Exercise: Participants develop models to detect fraud using simulated network traffic and transaction logs.
Case Study Discussion: Detailed review of a generative AI application in a high-profile fraud detection case.

Deployment & Compliance

Deployment Strategies: Best practices for deploying AI systems in a secure and scalable manner.
Maintaining Compliance with Financial Regulations: Ensuring that AI fraud detection systems comply with global financial regulations.
Evaluating AI Systems Post-Deployment: Tools and Techniques for Monitoring the Performance of AI Systems in Live Environments.
Final Project Presentations: Participants present their projects, discussing implementation, challenges, and outcomes.
Course Recap and Feedback: Summarizing key concepts, gathering participant feedback, and discussing the next steps for applying the learned skills.

AI-Driven Fraud Detection in Banking

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