Revolutionizing Fraud Detection: Innovative Strategies for Securing Transactions

This presentation explores advanced approaches in fraud detection, showcasing the latest strategies and technologies designed to combat fraudulent activities. Delve into the methods used to analyze and detect suspicious patterns in financial transactions and other critical domains. Learn about the integration of machine learning algorithms, data analytics, and real-time monitoring systems that enhance the accuracy and efficiency of fraud detection. Understand the challenges faced, such as managing large volumes of data and adapting to new fraud techniques, and see how innovative solutions are addressing these issues. Join us to discover how cutting-edge fraud detection systems are transforming security measures and protecting organizations from financial losses. to know more https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. Fraud Detection By: Aanchal Chhiba

2. Problem Statement This project aims to enhance the accuracy of detecting fraud in mobile financial transactions. By leveraging machine learning, the project seeks to predict fraudulent transactions with high precision. The goal is to develop a robust machine learning model to accurately identify fraudulent transactions in real-time , enabling the company to improve security, reduce financial losses, and gain insights into factors contributing to transaction fraud. Objective: Minimize Fraudulent Transactions

3. Data Overview Approximately 89.8% of the transactions are non-fraudulent, while 10.2% are fraudulent. The dataset contains 11,142 entries with 10 columns. Attributes: step : Time step of the transaction. type : Type of transaction (e.g., TRANSFER, CASH_OUT). amount : The amount of the transaction. nameOrig : The customer initiating the transaction.

4. DATA OVERVIEW Cont’d oldbalanceOrg : The initial balance of the customer before the transaction. newbalanceOrig : The balance of the customer after the transaction. nameDest : The recipient customer. oldbalanceDest : The initial balance of the recipient before the transaction. newbalanceDest : The balance of the recipient after the transaction. isFraud : Indicates whether the transaction was fraudulent (1 for fraud, 0 for non-fraud).

5. MACHINE LEARNING PIPELINE New User ML Model Fraud Non-Fraud ACTION

6. Exploratory Data Analysis (EDA) KEY INSIGHTS Class Imbalance Approximately 89.8% of the transactions are non-fraudulent, while 10.2% are fraudulent. This indicates a significant class imbalance in the dataset, which is important to consider for modeling and evaluation.

7. EDA Cont’d 2. Distribution of Transaction Types The most common transaction types are PAYMENT (5,510 transactions), followed by CASH_IN (1,951 transactions) and CASH_OUT (1,871 transactions). TRANSFER transactions (1,464) are less frequent but are more likely to be involved in fraudulent activities. DEBIT transactions (346) are the least common.

8. EDA Cont’d 3. Correlation Analysis ' oldbalanceOrg ' and ' newbalanceOrig ' have a very strong positive correlation (almost 1). This is expected as the new balance is derived from the old balance. Similarly, ' oldbalanceDest ' and ' newbalanceDest ' show a strong positive correlation. 'amount' and ' oldbalanceOrg ' have a moderate negative correlation, as do 'amount' and ' newbalanceOrig '. This suggests that larger transaction amounts tend to be associated with lower balances in the originating account 'amount' and ' isFraud ' have a weak positive correlation. This indicates that fraudulent transactions might involve slightly higher amounts on average.

9. Data Preprocessing 1. Handling Missing Values There are no missing values in this dataset. 2. One Hot Encoding We have performed one-hot encoding on the 'type' column. This function converts the categorical 'type' column into numerical features. It creates new columns for each unique value in the 'type' column, and assigns a 1 if the row has that value, and 0 otherwise. 3. Feature Scaling As my dataset contains some imbalance, I have applied feature scaling to standardize the data. Specifically, I used the Standard Scaler , which transforms the data so that the mean is 0 and the standard deviation is 1.

10. DATA PREPROCESSING 4. Feature Engineering Balance Differences and Account Status : Created features like ' balanceDiffOrig ' and ' balanceDiffDest ' to capture changes in balances for origin and destination accounts, and indicators like ' originAccountEmpty ' and ' destAccountEmpty ' to identify empty accounts after transactions, helping to detect fraud-related patterns. Transaction Type and High-Value Indicators : Introduced ' isTransferOrCashout ' to focus on high-risk transaction types (TRANSFER, CASH_OUT) and ' amountAboveThreshold ' to flag high-value transactions, which are more prone to fraud. Amount to Balance Ratios : Calculated ratios like ' amountToOldBalanceRatio ' to understand the transaction size relative to the account balance, offering insights into unusual transaction behaviors that may indicate fraud.

11. Machine Learning Model Evaluation

12. 1. Model Performance Overview : The Random Forest model achieved high accuracy and a strong AUC-ROC score, indicating good performance in distinguishing between fraudulent and non-fraudulent transactions. 2. Classification Metrics : Precision, Recall, and F1-Score are balanced, showing the model is effective in minimizing both false positives and false negatives. A high Recall (Sensitivity) indicates the model is good at identifying actual fraudulent transactions. 3. Confusion Matrix Insights : The confusion matrix reveals a low number of false positives and false negatives, which is crucial in fraud detection to avoid incorrect fraud alerts and missed fraud cases. 4. ROC AUC Curve: The ROC curve for the Random Forest classifier shows that the classifier has a high true positive rate and a low false positive rate. This indicates that the classifier is good at identifying fraudulent transactions without falsely flagging too many non-fraudulent transactions. Random Forest Classification

13. 1. Model Performance Overview : The Gradient Boosting model, using XGBoost , demonstrated strong predictive power with a high AUC-ROC score, indicating good discrimination between fraudulent and non-fraudulent transactions. 2.Classification Metrics : Precision and Recall scores are balanced, reflecting that the model is effective in correctly identifying fraud while minimizing false alarms. The F1-Score shows a good balance between precision and recall, especially critical in fraud detection. 3. Confusion Matrix Insights : The confusion matrix shows a low number of false positives and false negatives, indicating the model's robustness in identifying both fraudulent and legitimate transactions accurately. ROC AUC Curve The ROC curve and AUC score highlight the model's ability to differentiate between fraud and non-fraud transactions, with a high AUC indicating excellent performance. Gradient BOOSTING

14. 1. Model Performance Overview : The SVM model provided a balanced approach to detecting fraudulent transactions, showing good overall accuracy and AUC-ROC scores, which indicates effective separation between fraud and non-fraud cases. 2. Classification Metrics : Precision : The model maintained a high precision score, minimizing the number of false positives. Recall : High recall is crucial for fraud detection as it ensures most fraudulent cases are correctly identified. F1-Score : A good F1-Score balances precision and recall, demonstrating that the model is robust in detecting fraud without overly penalizing non-fraud transactions. 3. Confusion Matrix Insights : The confusion matrix illustrates a lower number of false positives and false negatives, highlighting the model's effectiveness in correctly identifying both fraudulent and legitimate transactions. 4. ROC-AUC Curve: The AUC-ROC curve shows a strong ability of the SVM model to discriminate between fraudulent and non-fraudulent transactions. A high AUC score further validates the model's performance. SUPPORT VECTOR MACHINE (SVM)

15. Model Comparison Based on the comparison, it appears that the Gradient Boosting model generally outperforms the other models in terms of accuracy, F1-score, precision and recall. It also has a high ROC AUC score, indicating its strong ability to distinguish between classes. Random Forest Classifier and SVM have also shown good performance. In this scenario, recall is the most important metric. In fraud detection, it's crucial to minimize false negatives. A false negative occurs when the model fails to identify a fraudulent transaction. This can lead to significant financial losses. Therefore, recall is preferred because it measures the model's ability to correctly identify all positive instances (fraudulent transactions). A high recall value indicates that the model is effectively capturing most of the fraudulent transaction.

16. Questions ?

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