Fortifying Fraud Detection: Advanced Data Analysis Techniques for Enhanced Security

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Fraud Detection Analysis
VARSHA

• Introduction
• Data collection & cleaning
• Exploratory data analysis
• Data pre-processing
• Model selection
• Model evaluation
• Conclusion

Introduction
A company called Block Fraud specializes in
identifying and preventing fraud in mobile financial
transactions. Their plan is to enter the Brazilian
market by implementing a competitive pricing
strategy leveraging their high accuracy in fraud
detection.

key metrics
• precision score : It is a metric that
measures the ability of a model to
identify relevant data points
• recall: a machine learning metric
that measures how often a model
correctly identifies positive
instances in a dataset.
• F1 score:It is a metric that
measures a model's performance
by combining precision and recall.

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Data cleaning
• Handled categorical features using one-hot
encoding.
• Checked for null values and ensured all
features relevant to modeling are numeric.
• These actions form a comprehensive data
cleaning process that prepares the dataset for
effective model training. Each step ensures
that the data is in the best possible shape to
train a robust and accurate model.

Outliers:

Exploratory Data Analysis

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Data pre-processing
• Standardized the dataset using
StandardScaler to prepare for model training.
• Applied StandardScaler to numerical features
to normalize the data, ensuring a mean of 0
and standard deviation of 1. This step helps
improve model performance, especially for
models sensitive to feature scaling.
• Identified and addressed outliers that could
skew the model’s predictions, either by
capping or removing extreme values.

Model training and Evaluation
• Chose Logistic Regression as the model for predicting fraud, a suitable
choice given the binary nature of the target variable (isFraud).
• Split the dataset into training and testing sets using train_test_split,
ensuring that the model is trained on one portion of the data and
evaluated on another to gauge its performance on unseen data.
• Model Training: Applied StandardScaler to the features in both training
and testing sets to standardize the input data, ensuring the model treats
each feature equally regardless of scale

Model Training:
Trained the Logistic Regression model on the preprocessed and
scaled training data.
Fit the model using X_train and y_train, optimizing the model's
parameters on the training data.
Accuracy Score: Evaluated the model’s performance by calculating
the accuracy on the test set, comparing predicted labels with
actual labels in y_test.

• Confusion Matrix: Analyzed the confusion matrix to
understand the number of true positives, true negatives,
false positives, and false negatives.
• Precision, Recall, F1-Score: Assessed precision, recall, and
F1-score to get a balanced view of model performance,
especially considering class imbalance (if any).

By using Logistic Regression Model
• Confusion Matrix:
True Negatives (TN): 1999
False Positives (FP): 0
False Negatives (FN): 27
True Positives (TP): 203
• Accuracy: 98.79%
• Classification Report:
• Precision for Class 1 (Fraud): 1.00
• Recall for Class 1 (Fraud): 0.88

Financial Impact Analysis
Revenue from True Positives (Correctly Identified Fraudulent
Transactions):
Revenue per Correct Fraudulent Transaction: 25% of the transaction value
True Positives (TP): 203
Total Revenue: 203 × 25 % ×
average fraud transaction value
203×25%×average fraud transaction value

2.Costs from False Negatives (Missed Fraudulent Transactions):
Cost per Missed Fraudulent Transaction: 100% refund of the transaction value
False Negatives (FN): 27
Total Cost: 27×100%×V
3.Costs from False Positives (Incorrectly Identified Legitimate Transactions):
Cost per Incorrectly Identified Legitimate Transaction: 5% charge
False Positives (FP): 0 (So, the cost is $0 in this case)
4. Net Financial Impact:
The Net Financial Impact can be calculated by subtracting the total costs (from False
Negatives and False Positives) from the total revenue (from True Positives).
Net Financial Impact=(203×0.25×V)−(27×1×V)

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Conclusion
• In this project, developed a model to detect
fraudulent transactions, focusing on thorough data
preparation and feature engineering.
• The net financial impact is positive, implying that your
model has a beneficial effect on the company's
revenue. For every fraudulent transaction detected,
the company earns a significant portion of the
transaction value, minus the costs associated with
missed frauds.
• If you know the average value of fraudulent
transactions (V), you can substitute it into the formula
to get the exact financial impact
.

Thank You!

Fortifying Fraud Detection: Advanced Data Analysis Techniques for Enhanced Security

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