Fraud Detection Using AI in Finance
Leveraging AI to Secure Finance: Detect and Prevent Fraud in Real-Time
About This Course
This course focuses on using AI-driven tools to detect financial fraud by identifying anomalies and unusual patterns in transaction data. Participants will learn machine learning algorithms and AI-based techniques to build systems for fraud prevention and financial security.
Aim
To equip PhD scholars and academicians with advanced skills in utilizing AI and machine learning to detect and prevent fraud in financial services. This course covers anomaly detection, fraud prevention, and the use of AI models to identify suspicious activities in real-time.
Workshop Objectives
- Understand AI’s role in detecting financial fraud.
- Build machine learning models for anomaly detection.
- Apply AI techniques to identify suspicious financial activities.
- Design fraud prevention strategies with real-time detection.
- Gain hands-on experience in building AI-driven fraud detection systems.
Workshop Structure
Day 1: Introduction to AI in Financial Fraud Detection
Objective: Introduce participants to the basics of AI technologies used for detecting fraud in financial services.
Session Details:
- Understanding Financial Fraud: Types of fraud encountered in financial services including credit card fraud, insurance fraud, and cyber fraud.
- Role of AI in Fraud Detection: Overview of how AI can enhance fraud detection and the types of AI technologies used.
- Introduction to Machine Learning: Basic concepts and algorithms commonly used in fraud detection (e.g., classification, clustering).
- Hands-On Activity: Setting up the machine learning environment and exploring initial financial datasets.
Day 2: Machine Learning Techniques for Fraud Detection
Objective: Dive deeper into specific machine learning techniques and their applications in detecting financial fraud.
Session Details:
- Feature Engineering for Fraud Detection: Techniques for selecting and engineering features from transactional data that are indicative of fraudulent activities.
- Anomaly Detection Techniques: Implementing algorithms like isolation forests, neural networks, and unsupervised learning methods to identify unusual patterns.
- Model Training and Evaluation: Training models on historical fraud data and evaluating their performance.
- Hands-On Activity: Building a machine learning model to identify potential fraud in a set of transactional data.
Day 3: Implementing and Optimizing Fraud Detection Models
Objective: Learn how to deploy, monitor, and optimize AI models in real-world financial settings.
Session Details:
- Deploying AI Models: Strategies for integrating AI fraud detection models into existing financial systems.
- Model Monitoring and Updating: Techniques for monitoring model performance over time and updating models to adapt to new fraudulent tactics.
- Case Studies: Discussion of real-world applications and success stories in AI-driven fraud detection.
- Hands-On Activity: Simulating the deployment of a fraud detection model and monitoring its alerts on sample financial transactions.
Who Should Enrol?
Finance professionals, data scientists, risk analysts, cybersecurity experts, and academic researchers.
Important Dates
Registration Ends
11/21/2024
IST 1:00 pm
Workshop Dates
11/21/2024 – 11/23/2024
IST 6 PM
Workshop Outcomes
- Develop AI-driven systems for real-time fraud detection.
- Identify anomalies and suspicious activities in financial transactions.
- Build and apply machine learning models for fraud prevention.
- Implement risk management strategies using AI tools.
- Gain hands-on experience in financial fraud detection projects.
Fee Structure
Student Standard fee
₹1499 | $40
Ph.D. Scholar / Researcher Standard fee
₹1999 | $45
Academician / Faculty Standard fee
₹2999 | $50
Industry Professional Standard fee
₹4999 | $75
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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