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fintech Advanced 21 lessons

Fraud Detection ML

Catch bad actors with AI. Master Anomaly Detection, Supervised Learning for fraud classification, and feature engineering for risk.

Fraud is a multi-billion dollar problem. This course teaches you to build Machine Learning models to detect fraudulent transactions. You will handle highly imbalanced datasets (where fraud is rare) using techniques like SMOTE. Master Feature Engineering to create signals from timestamps, IP addresses, and user behavior. We cover algorithms like Isolation Forests for anomaly detection and XGBoost for classification. Essential for data scientists in banking and payments.

✅ 100% Free & Lifetime Access
⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
📱 Mobile Friendly (Learn anywhere)
Risk Data
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Complete Course Syllabus

  • 1
    The Fraud Dataset
    Understanding imbalance and the cost of False Positives.
  • 2
    Feature Engineering
    Creating 'velocity' and 'distance' features from logs.
  • 3
    Supervised Learning
    Training classifiers (Random Forest/XGBoost).
  • 4
    Unsupervised Learning
    Finding new fraud patterns with Isolation Forests.
  • 5
    Evaluation
    Why Accuracy is a bad metric for fraud models.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$130k - $180k

Career Paths

Fraud Data Scientist $135k-$185k
Risk Engineer $125k-$170k
ML Engineer $130k-$180k

What You Will Learn

Train ML models to classify fraudulent vs legitimate transactions
Handle imbalanced datasets using oversampling techniques
Engineer features from raw transaction logs for better signal
Deploy real-time fraud scoring APIs
Evaluate models using Precision-Recall instead of Accuracy

Skills You Will Gain

Machine Learning Fraud Analysis Imbalanced Data Feature Engineering Scikit-Learn

Who Is This For

Data Scientists
Risk Analysts
ML Engineers

Prerequisites

Machine Learning
Python

Fraud Detection ML FAQs

Real data?

We use anonymized credit card datasets (Kaggle).

Deep Learning?

Possible, but Tree-based models often win in tabular fraud.

Real-time?

We discuss serving models for ms-level scoring.

Prereqs?

Must know Python and ML basics.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

Python
Pandas
Scikit-Learn
XGBoost

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