CONTACT FOR DEMO CLASS : (+91) 8951869553/52
CONTACT FOR DEMO CLASS : (+91) 8951869553/52
Course Start Date: TBD
This project focuses on detecting fraudulent financial transactions using machine learning techniques. It analyzes transaction patterns, identifies anomalies, and classifies transactions as genuine or fraudulent using real-world financial datasets.
Learn to use Generative AI tools for smart lead generation, personalized outreach, and automated sales growth.
No boring theory. Every session includes real tools, real prompts, real workflows, real AI agents, giving you skills you can use the same day.
AI evolves every week. Micro-learning ensures you stay updated with latest tools, frameworks, and use-cases without wasting time on outdated content.
Micro-sessions fit perfectly into your day
In this power-packed session, participants will learn how to apply
Machine Learning techniques to
detect fraudulent financial transactions.
This micro session is designed for students, finance professionals, analysts,
and developers who want to understand how
fraud detection systems work in real-world banking and fintech environments.
Through real-time demonstrations and
guided hands-on activities, participants will work with
financial transaction datasets, build
fraud detection models, and understand how Machine Learning
identifies anomalies, suspicious patterns, and high-risk transactions.
By the end of the session, learners will gain
practical skills, reusable fraud detection techniques,
and an end-to-end framework that can be applied to
real-world fraud detection scenarios.
Through real-time demonstrations and guided hands-on activities, participants will work with financial datasets, build predictive models, and understand how AI enhances forecasting accuracy and decision-making. By the end of the session, learners will gain practical knowledge, reusable forecasting techniques, and an actionable framework that can be immediately applied in real-world financial scenarios.
EarlyRise's Fraud Detection using Machine Learning – Micro Session Key Features
Understand how fraudulent transactions occur in real financial systems
Detect suspicious and fraudulent transactions using Machine Learning models
Work with real-world financial transaction datasets
Build reusable end-to-end fraud detection workflows
Improve risk identification and fraud prevention capabilities
Complete a practical Machine Learning–based fraud detection project
Customized to your team's needs
Key Learning Objective: Understand how Machine Learning is used to detect fraudulent financial transactionsby analyzing transaction behavior, identifying suspicious patterns, and distinguishing fraudulent activity from legitimate transactions.
Key Learning Objective: Learn how to clean, preprocess, and structure financial transaction data for fraud detection, including handling missing values, feature scaling, and addressing highly imbalanced datasets
Key Learning Objective: Understand and differentiate key financial transaction data types such as transaction amounts, frequency, merchant information, location data, timestamps, and user behavior signals that are commonly used in Machine Learning–based fraud detection systems.
Key Learning Objective: Learn how to interpret fraud signals and use Machine Learning techniques to identify suspicious patterns, anomalies, and high-risk transactions that may indicate potential fraud.
Key Learning Objective: Learn how to design effective Machine Learning models for fraud detection by selecting relevant transaction features, choosing suitable algorithms, and structuring inputs that help accurately classify fraudulent and legitimate transactions.
Key Learning Objective: Understand how anomaly detection techniques identify unusual transaction behavior and how different fraud scenarios such as high-risk, medium-risk, and low-risk transactions are analyzed using Machine Learning models.
Key Learning Objective: Understand ethical considerations, data bias, false positives, and reliability challenges in Machine Learning–based fraud detection systems to ensure responsible and fair decision-making.
Key Learning Objective: Learn how to evaluate Machine Learning–based fraud detection models using industry-standard metrics such as Precision, Recall, F1-Score, and ROC-AUC, and understand why accuracy alone is not sufficient for fraud detection systems
Key Learning Objective: Understand the complete end-to-end fraud detection workflow, including:
4 Hours
4 Hours
Upon successful completion of the course, participants will receive a certificate from EarlyRise. This certificate is widely recognized and signifies that the holder has acquired specialized skills.
Get In Touch
Our team will be happy to assist you make the right decision
Leading industry professionals who bring current best practices and case studies to sessions that fit into your work schedule.
Our Course fees are very nominal and competitive. We provide Scholarship up to 50% time to time for eligible candidates.
This session is designed for students, software developers, data science aspirants, finance professionals, and anyone interested in understanding how Machine Learning is used to detect fraudulent financial transactions in real-world systems.
No prior experience is required. The micro session starts with the fundamentals and gradually introduces Machine Learning concepts for fraud detection in a beginner-friendly and hands-on manner.
After completing the session, you will be able to understand fraud patterns, analyze transaction data, apply Machine Learning models for fraud detection, and interpret fraud risk scores used in real banking and fintech systems.
This session is highly practical. Participants will work with real-world financial transaction datasets, explore fraud scenarios, and understand how Machine Learning models detect suspicious activities.
Yes. Participants will receive sample transaction datasets, learning resources, and structured guidance that can be used to practice and apply fraud detection techniques using Machine Learning.
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