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Fraud detection with Machine Learning

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Scaleway
Scaleway

ScaleDay conference

Technology
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1
Arnaud Wald MACHINE LEARNING ENGINEER FRAUD DETECTION WITH MACHINE LEARNING 2
WHAT IS FRAUD ? 3
4 Stolen Credit Card → Unpaid resources FRAUD @ SCALEWAY
DEALING WITH FRAUD Quotas Manual Intervention 5
6 Frequency Magnitude Sophistication INCREASING TRENDS
THE IDEAL SOLUTION 7 While having a low false positive rate Fast Scalable Adaptable
IS THERE A SOLUTION? 8
MACHINE LEARNING 9 While having a low false posiJve rate Fast Scalable Adaptable✓ ✓ ✓ ✓
MACH IN E LEARN IN G PRO JECT 10
OBJECTIVE 11 ML MODEL Fraud Non Fraud New User ACTION
MACHINE LEARNING PIPELINE 12 ML MODEL Fraud Non Fraud New User ACTION
ML PIPELINE 13 Data Preparation Model Building & Training Model Deployment
WHERE DO WE GET THE DATA ? 14
GATHERING DATA 15 Email ToS Phone Address Credit Card 2FA Start Instance SSH
CHOOSING A MACHINE LEARNING MODEL 16
Deep Learning - Very Powerful - Needs lots (millions) of data Classic ML: Random Forest Gradient Boosting Choosing a Machine Learning Algorithm 17 MODEL SELECTION Chosen algorithm: CatBoost
- Number of trees - Depth of trees - Learning rate 18 HYPERPARAMETERS Example from Titanic data Source: Wikipedia
CatBoost brings: - Powerful - Open-source - Handles categorical data very well - Automatic overfitting detection - (Works on GPU) 19 CATBOOST
EVALUATING PERFORMANCE 20
Trade-off between being too severe/ Being too friendly Avoiding Overfitting False positives > Innocents wrongly flagged False Negatives > Fraudsters not detected EVALUATING PERFORMANCE Hyperparameter Tuning 21
MACHINE LEARNING PIPELINE 22 ML MODEL Fraud Non Fraud New User ACTION
GOING FURTHER 23
NEW PRO D U CTS 24 OTHER FRAUD BEHAVIORS
Stay tuned for exclusive tutorials and updates, follow us on Twitter and LinkedIn @Scaleway THANK YOU ! And follow me on LinkedIn @ArnaudWald
26

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