Fraud Analysis of Transactions: Identifying and Mitigating Risks

"This presentation offers a comprehensive analysis of transaction fraud. Presented by students from the Boston Institute of Analytics, this report delves into the patterns and indicators of fraudulent activity, highlighting key risks and vulnerabilities in transaction processes. We will explore methodologies for detecting fraudulent transactions, analyze case studies, and propose effective strategies for fraud prevention and mitigation. Our goal is to provide actionable insights to enhance security measures and safeguard both businesses and consumers from financial losses and reputational damage. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

1. Fraud Detection of Transactions using Machine Learning Aswin Kumar M N BIA – HSR Campus Bengaluru

2. Agenda Introduction Dataset and Exploratory Data Analysis Feature Engineering Model Selection and Performance Evaluation Financial Impact Analysis Visualization and Insights Questions

3. Introduction This project focuses on developing a machine learning model to detect fraudulent transactions in mobile financial systems. The primary goal is to enhance transaction security by accurately identifying fraudulent activities in real-time. By leveraging data analysis, feature engineering, and robust model evaluation techniques, this project aims to deliver a production-ready solution that minimizes financial losses due to fraud, while providing valuable insights into the factors contributing to fraudulent transactions.

4. Dataset & EDA The dataset used in this project contains transactional data including various features like transaction amounts, timestamps. It also includes a labeled indicator of whether each transaction is fraudulent or not. The data is imbalanced, with a significantly lower number of fraudulent transactions compared to legitimate ones. Preprocessing steps involved handling missing values, normalizing features, and encoding categorical variables to prepare the data for model training.

5. Exploratory Data Analysis Understanding the prevalence of each transaction type in our dataset, which can be crucial for identifying patterns related to fraud. The chart helps in highlighting which types of transactions are more prone to fraud, helping to identify patterns and focus areas for the fraud detection model. This box plot helps to identify outliers in transaction amounts, which can be crucial in detecting anomalous transactions that might indicate fraud.

6. Exploratory Data Analysis This analysis is vital for understanding typical transaction behaviors and identifying any anomalies that may indicate fraud. Correlation heatmap will visually highlight the relationships between different numeric features, helping to identify strong correlations that could inform feature selection and engineering

7. Feature Engineering Feature engineering is vital for enhancing the performance of our fraud detection model. It involves identifying and creating relevant features, such as transaction frequency and average transaction amount, to capture complex patterns. We also encode categorical variables for numerical analysis, normalize numerical features, and handle missing values to ensure data integrity. Outlier has been checked By thoughtfully engineering features, we improve the model's ability to accurately identify fraudulent transactions and enhance overall predictive accuracy. Data has also been split into Test and Train for the evaluation purpose

8. Model Selection and Performance Evaluation We explored various classifiers, including Logistic Regression, Random Forest, Gradient Boosting, Decision trees and KNN. Each algorithm has its strengths; for instance, Random Forest is robust to overfitting and can handle large datasets well, while Gradient Boosting often delivers high accuracy through ensemble learning. To assess model performance, focused on several key metrics: Precision : Measures the accuracy of positive predictions (fraudulent transactions). Recall : Indicates the model's ability to identify all actual fraudulent transactions. F1-Score : The harmonic mean of precision and recall, providing a balance between the two metrics. ROC-AUC : Evaluates the model’s ability to distinguish between classes, with a higher score indicating better performance. Confusion Matrix : We utilized a confusion matrix to visualize the model's predictions against actual outcomes, helping us understand the number of true positives, false positives, true negatives, and false negatives. This information is crucial for diagnosing model weaknesses.

9. Model Selection and Performance Evaluation

10. Financial Impact Analysis Model's Precision and Accuracy The ROC curve further supports this, displaying an area under the curve (AUC) of 0.97, which indicates excellent overall performance in distinguishing between fraudulent and legitimate transactions. Model's Reliability The model is highly reliable in classifying transactions. The ROC curve with an AUC of 0.97 suggests that the model has a strong ability to distinguish between fraudulent and non-fraudulent transactions across various thresholds. Potential Losses Due to Model Errors The potential losses due to model errors can be inferred from the 18 false negatives in the confusion matrix, where actual fraudulent transactions were not detected. These missed frauds could result in financial losses.

11. Visualization and Insights

12. Real Time App The app is designed to predict whether a financial transaction is fraudulent based on several features such as the transaction amount, balance differences, transaction types, and more. It uses a machine learning model to make these predictions.

13. Questions ?

14. Thank You!