Fortifying Fraud Detection: Advanced Data Analysis Techniques for Enhanced Security

Dive into the world of data analysis and its crucial role in combating fraud across various sectors. This presentation, crafted by Varsha student from the Boston Institute of Analytics, highlights sophisticated data analysis techniques designed to detect and prevent fraudulent activities. Learn how to leverage machine learning algorithms, statistical models, and real-time analytics to identify suspicious patterns and anomalies. This session provides valuable insights into enhancing security measures and safeguarding financial transactions, making it a must-see for anyone interested in the intersection of data science and fraud prevention. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. Fraud Detection Analysis VARSHA

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

3. 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.

4. 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.

5. 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.

6. Outliers:

7. Exploratory Data Analysis

8. 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.

9. 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

10. 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.

11. 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).

12. 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

13. 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

14. 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)

15. 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 .

16. Thank You!