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Credit Card Fraud Detection_ Mansi_Choudhary.pptx

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Boston Institute of Analytics

Dive into the intricate world of fraud detection with this comprehensive presentation featuring an unique student project. Explore the project's objectives, methodologies, and innovative solutions developed to combat fraudulent activities within financial transactions. From data analysis to model implementation, witness the journey our student has undertaken to create a robust fraud detection system. Whether you're a fellow student, industry professional, or enthusiast, this showcase provides valuable insights into the challenges and advancements in fraud detection technology. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.

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Credit Card Fraud Detection By: Mansi Choudhary
Overview Introduction 01 Literature Review 02 Data Collection 03 Data Preprocessing 04 Feature Selection 05 Machine Learning Models 06 Anomaly Detection Techniques 07 Deep Learning Techniques 08 Evaluation Metrics 09
INTRODUCTION Overview of credit card fraud and its impact on individuals and businesses : • Credit card fraud is a type of financial fraud. It involves the unauthorised use of someone's credit card information to make fraudulent transactions, leading to financial losses for the cardholder. It is a form of identity theft because it often requires stolen personal information to perpetrate the fraud • Fraud can also hurt customer retention and decrease customer lifetime value (LTV). If a customer disputes a charge on their credit card bill, the business may be required to pay a chargeback fee. Additionally, many payment processing providers charge additional fees to businesses that have a higher chargeback ratio
Significance of developing effective fraud detection methods 1.Protecting Finances: Fraud can result in significant financial losses for individuals, businesses, and governments. Effective detection methods help minimize these losses by identifying fraudulent activities early on. 2.Maintaining Trust: Fraud can erode trust between customers, businesses, and financial institutions. By implementing robust fraud detection measures, organizations can demonstrate their commitment to protecting their customers and stakeholders, thereby preserving trust and reputation. 3.Compliance: Many industries are subject to regulatory requirements regarding fraud prevention and detection. Implementing effective fraud detection methods helps organizations comply with these regulations, avoiding legal penalties and reputational damage 4.Data Security: Fraud detection often involves monitoring and analyzing large volumes of data. Developing effective fraud detection methods necessitates robust data security measures to protect sensitive information from unauthorized access and misuse. 5.Customer Protection: Effective fraud detection methods help protect customers from identity theft, financial scams, and other fraudulent activities. This enhances customer satisfaction and loyalty, as customers feel safer and more confident conducting transactions with the organization
Statistics
LITERATURE REVIEW Below is link for existing literature on credit card fraud detection : Existing literature on credit card fraud detection techniques Models used in previous research paper: • Cleaned the data • They acquired the result of an accurate value of credit card fraud detection i.e. 0.9994802867383512 (99.93%) using a random forest algorithm.
Strengths: • They acquired the result of an accurate value of credit card fraud detection i.e. 0.9994802867383512 (99.93%) using a random forest algorithm. Limitations : • Imbalanced data is not handled • Pre Processing like Standardization or Normalization not performed • Neural network not included • Data Visualizations was not good enough
DATA COLLECTION Data Collected from Kaggle https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud Discuss the importance of having labeled data: Labeled data is crucial for training and evaluating machine learning models. It provides the ground truth necessary for algorithms to learn patterns, make predictions, and assess performance accurately. Labeled data enables model development, validation, and generalization to new scenarios, ensuring reliable and effective machine learning applications
DATA PREPROCESSING Steps: • Loaded the dataset • Checking for null values • Checking for duplicates value • Delete the non important features • Feature Selection • Standard Scaler • SMOTE • ML & DL
Data Cleaning After removing duplicates values No null values
All data types was in float no need to do label encoding or 1-hot encoding
Highly imbalanced data SMOTE performed
• Feature selection is a process in data science where the most relevant and informative features (or variables) are chosen from the original set of features in a dataset. • The goal is to improve model performance, reduce overfitting, and enhance interpretability by focusing on the most predictive and meaningful features FEATURE SELECTION
Feature selection Techniques aim Reduce Dimensionality Improve Model Performance Enhance Interpretability Avoid Overfitting
F-test (f_classif) for feature selection is performed to select the best features f_classif is a function used for performing analysis of variance (ANOVA) F-test for feature selection in classification tasks. It computes the ANOVA F-value for each feature and its corresponding p-value. These values help determine the significance of each feature in discriminating between different classes in a classification problem
F-test (f_classif)
Remove unwanted features
MACHINE LEARNING MODELS 03 Logistic Regression Random Forest Classifier Decision Tree Classifier Neural Network
Logistic Regression Decision Tree Classifier
Random Forest Classifier
Neural Network Accuracy :99 %
ANOMALY DETECTION TECHNIQUES 03 Logistic Regression SVC KNeighbor s Classifier GaussianN B Random Forest Classifier Decision Tree Classifier Neural Network Anomaly detection refers to the process of identifying patterns or instances that deviate from the norm within a dataset. This technique is particularly useful in various fields, including fraud detection, network security, healthcare monitoring, and manufacturing quality control, among others. When it comes to detecting rare fraud cases, anomaly detection approaches play a crucial role because fraudulent activities often represent a small proportion of overall transactions or events.
Common anomaly detection approaches 01 Isolation Forest 02 One-Class SVM 03 Local Outlier Factor
Credit Card Fraud Detection_ Mansi_Choudhary.pptx
DEEP LEARNING TECHNIQUES 03 Logistic Regression SVC KNeighbor s Classifier GaussianN B Random Forest Classifier Decision Tree Classifier Neural Network Deep learning models, including deep neural networks (DNNs), have been increasingly used for credit card fraud detection due to their ability to learn complex patterns and features from large-scale and high-dimensional data
Potential Advantages: 1.Feature Learning: Deep learning models can automatically learn hierarchical representations of data, enabling them to capture complex patterns without explicit feature engineering. 2.Adaptability: Deep learning models can adapt to changing fraud patterns and evolving fraud tactics by continuously learning from new data. 3.High Performance: Deep learning models have demonstrated high performance in various complex tasks, suggesting their potential to achieve state-of-the-art results in credit card fraud detection. 4.Scalability: Deep learning models can scale effectively to large datasets, making them suitable for processing massive volumes of credit card transactions in real-time.
Challenges: 1.Data Quality and Quantity: Deep learning models require large amounts of high-quality labeled data for training, which may be scarce in fraud detection due to the rarity of fraudulent instances. 2.Interpretability: Deep learning models are often regarded as "black boxes," making it challenging to interpret their decisions, which can be a concern in highly regulated domains like finance. 3.Computational Complexity: Training deep learning models, especially large architectures, can be computationally intensive and time-consuming, requiring significant computational resources. 4.Overfitting: Deep learning models are susceptible to overfitting, especially when trained on imbalanced datasets with a small number of fraud cases. Regularization techniques and careful model evaluation are necessary to mitigate this issue.
EVALUATION METRICS 03 Logistic Regression SVC KNeighbor s Classifier GaussianN B Random Forest Classifier Decision Tree Classifier Neural Network 1.Accuracy: Accuracy measures the overall correctness of the model's predictions and is calculated as the ratio of correctly predicted instances to the total number of instances. However, accuracy alone may not be a reliable metric for imbalanced datasets where the number of fraudulent transactions is much smaller than legitimate ones. 2.Precision: Precision measures the proportion of true positives (correctly identified fraudulent transactions) among all transactions predicted as fraudulent. It is calculated as the ratio of true positives to the sum of true positives and false positives (legitimate transactions incorrectly classified as fraudulent). High precision indicates a low false positive rate, which is desirable in fraud detection to minimize the number of false alarms.Precision = True Positives / (True Positives + False Positives) 3.Recall (Sensitivity): Recall measures the proportion of true positives among all actual fraudulent transactions. It is calculated as the ratio of true positives to the sum of true positives and false negatives (fraudulent transactions incorrectly classified as legitimate). High recall indicates a low false negative rate, ensuring that most fraudulent transactions are detected. Recall = True Positives / (True Positives + False Negatives)
Recall could be more important in detecting potentially harmful security threats Finally observed that Logistic regression, decision tree and random forest are the algorithms that gave better results
Real-time Implementation and Challenges Implementing fraud detection models in real-time payment processing systems presents several challenges and considerations, including model interpretability, computational efficiency, and handling concept drift Aspects: Model Interpretability: • Challenge: Deep learning models, which are increasingly used in fraud detection, are often regarded as black boxes, making it difficult to understand and interpret their decisions. This lack of interpretability can hinder trust and regulatory compliance
Computational Efficiency: • Challenge: Real-time payment processing systems require low-latency responses to handle large transaction volumes. Complex models with high computational requirements may not meet the real-time processing constraints Handling Concept Drift: • Challenge: Concept drift refers to the phenomenon where the statistical properties of the data change over time, leading to degradation in model performance if not addressed. In fraud detection, fraudsters constantly adapt their tactics, causing the fraud detection model's effectiveness to degrade over time
Conclusion • In this project, I devised a novel approach for fraud detection. Utilizing the Synthetic Minority Over-sampling Technique (SMOTE), I balanced the dataset, leading to notable improvements in classifier performance. Moreover, I conducted feature scaling to enhance results, and feature selection to eliminate irrelevant features. Employing a range of techniques including Machine Learning, Deep Neural Networks, and Anomaly Detection methods, I observed that Logistic Regression, Decision Trees, Random Forest, and Deep Neural Networks emerged as the top- performing algorithms. • In conclusion, through rigorous experimentation and analysis, it became evident that Logistic Regression, Decision Trees, Random Forest, and Deep Neural Networks exhibit superior performance in detecting fraudulent activities.
Discuss potential areas of improvement and future research directions in credit card fraud detection. 1.Enhanced Feature Engineering: Continuous exploration and identification of new features that capture subtle patterns indicative of fraudulent behavior can improve detection accuracy. This may involve incorporating additional transaction metadata, behavioral biometrics, or contextual information associated with transactions. 2.Dynamic Risk Scoring: Developing dynamic risk scoring mechanisms that adapt to evolving fraud patterns in real- time can enhance detection capabilities. Techniques such as adaptive risk scoring models that continuously update risk scores based on recent transaction data and fraud trends can improve fraud detection efficiency. 3.Utilization of Advanced Machine Learning Techniques: Continued exploration and adoption of advanced machine learning techniques, such as ensemble methods, deep learning architectures, and reinforcement learning, can improve fraud detection accuracy, especially in handling complex fraud patterns and large-scale datasets. 4.Unsupervised and Semi-Supervised Learning: Leveraging unsupervised and semi-supervised learning approaches for anomaly detection in credit card transactions can be beneficial, particularly for detecting emerging fraud patterns and previously unseen attacks without relying heavily on labeled data.
Credit Card Fraud Detection_ Mansi_Choudhary.pptx

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Credit Card Fraud Detection_ Mansi_Choudhary.pptx

  • 2. Overview Introduction 01 Literature Review 02 Data Collection 03 Data Preprocessing 04 Feature Selection 05 Machine Learning Models 06 Anomaly Detection Techniques 07 Deep Learning Techniques 08 Evaluation Metrics 09
  • 3. INTRODUCTION Overview of credit card fraud and its impact on individuals and businesses : • Credit card fraud is a type of financial fraud. It involves the unauthorised use of someone's credit card information to make fraudulent transactions, leading to financial losses for the cardholder. It is a form of identity theft because it often requires stolen personal information to perpetrate the fraud • Fraud can also hurt customer retention and decrease customer lifetime value (LTV). If a customer disputes a charge on their credit card bill, the business may be required to pay a chargeback fee. Additionally, many payment processing providers charge additional fees to businesses that have a higher chargeback ratio
  • 4. Significance of developing effective fraud detection methods 1.Protecting Finances: Fraud can result in significant financial losses for individuals, businesses, and governments. Effective detection methods help minimize these losses by identifying fraudulent activities early on. 2.Maintaining Trust: Fraud can erode trust between customers, businesses, and financial institutions. By implementing robust fraud detection measures, organizations can demonstrate their commitment to protecting their customers and stakeholders, thereby preserving trust and reputation. 3.Compliance: Many industries are subject to regulatory requirements regarding fraud prevention and detection. Implementing effective fraud detection methods helps organizations comply with these regulations, avoiding legal penalties and reputational damage 4.Data Security: Fraud detection often involves monitoring and analyzing large volumes of data. Developing effective fraud detection methods necessitates robust data security measures to protect sensitive information from unauthorized access and misuse. 5.Customer Protection: Effective fraud detection methods help protect customers from identity theft, financial scams, and other fraudulent activities. This enhances customer satisfaction and loyalty, as customers feel safer and more confident conducting transactions with the organization
  • 6. LITERATURE REVIEW Below is link for existing literature on credit card fraud detection : Existing literature on credit card fraud detection techniques Models used in previous research paper: • Cleaned the data • They acquired the result of an accurate value of credit card fraud detection i.e. 0.9994802867383512 (99.93%) using a random forest algorithm.
  • 7. Strengths: • They acquired the result of an accurate value of credit card fraud detection i.e. 0.9994802867383512 (99.93%) using a random forest algorithm. Limitations : • Imbalanced data is not handled • Pre Processing like Standardization or Normalization not performed • Neural network not included • Data Visualizations was not good enough
  • 8. DATA COLLECTION Data Collected from Kaggle https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud Discuss the importance of having labeled data: Labeled data is crucial for training and evaluating machine learning models. It provides the ground truth necessary for algorithms to learn patterns, make predictions, and assess performance accurately. Labeled data enables model development, validation, and generalization to new scenarios, ensuring reliable and effective machine learning applications
  • 9. DATA PREPROCESSING Steps: • Loaded the dataset • Checking for null values • Checking for duplicates value • Delete the non important features • Feature Selection • Standard Scaler • SMOTE • ML & DL
  • 10. Data Cleaning After removing duplicates values No null values
  • 11. All data types was in float no need to do label encoding or 1-hot encoding
  • 13. • Feature selection is a process in data science where the most relevant and informative features (or variables) are chosen from the original set of features in a dataset. • The goal is to improve model performance, reduce overfitting, and enhance interpretability by focusing on the most predictive and meaningful features FEATURE SELECTION
  • 15. F-test (f_classif) for feature selection is performed to select the best features f_classif is a function used for performing analysis of variance (ANOVA) F-test for feature selection in classification tasks. It computes the ANOVA F-value for each feature and its corresponding p-value. These values help determine the significance of each feature in discriminating between different classes in a classification problem
  • 18. MACHINE LEARNING MODELS 03 Logistic Regression Random Forest Classifier Decision Tree Classifier Neural Network
  • 22. ANOMALY DETECTION TECHNIQUES 03 Logistic Regression SVC KNeighbor s Classifier GaussianN B Random Forest Classifier Decision Tree Classifier Neural Network Anomaly detection refers to the process of identifying patterns or instances that deviate from the norm within a dataset. This technique is particularly useful in various fields, including fraud detection, network security, healthcare monitoring, and manufacturing quality control, among others. When it comes to detecting rare fraud cases, anomaly detection approaches play a crucial role because fraudulent activities often represent a small proportion of overall transactions or events.
  • 23. Common anomaly detection approaches 01 Isolation Forest 02 One-Class SVM 03 Local Outlier Factor
  • 25. DEEP LEARNING TECHNIQUES 03 Logistic Regression SVC KNeighbor s Classifier GaussianN B Random Forest Classifier Decision Tree Classifier Neural Network Deep learning models, including deep neural networks (DNNs), have been increasingly used for credit card fraud detection due to their ability to learn complex patterns and features from large-scale and high-dimensional data
  • 26. Potential Advantages: 1.Feature Learning: Deep learning models can automatically learn hierarchical representations of data, enabling them to capture complex patterns without explicit feature engineering. 2.Adaptability: Deep learning models can adapt to changing fraud patterns and evolving fraud tactics by continuously learning from new data. 3.High Performance: Deep learning models have demonstrated high performance in various complex tasks, suggesting their potential to achieve state-of-the-art results in credit card fraud detection. 4.Scalability: Deep learning models can scale effectively to large datasets, making them suitable for processing massive volumes of credit card transactions in real-time.
  • 27. Challenges: 1.Data Quality and Quantity: Deep learning models require large amounts of high-quality labeled data for training, which may be scarce in fraud detection due to the rarity of fraudulent instances. 2.Interpretability: Deep learning models are often regarded as "black boxes," making it challenging to interpret their decisions, which can be a concern in highly regulated domains like finance. 3.Computational Complexity: Training deep learning models, especially large architectures, can be computationally intensive and time-consuming, requiring significant computational resources. 4.Overfitting: Deep learning models are susceptible to overfitting, especially when trained on imbalanced datasets with a small number of fraud cases. Regularization techniques and careful model evaluation are necessary to mitigate this issue.
  • 28. EVALUATION METRICS 03 Logistic Regression SVC KNeighbor s Classifier GaussianN B Random Forest Classifier Decision Tree Classifier Neural Network 1.Accuracy: Accuracy measures the overall correctness of the model's predictions and is calculated as the ratio of correctly predicted instances to the total number of instances. However, accuracy alone may not be a reliable metric for imbalanced datasets where the number of fraudulent transactions is much smaller than legitimate ones. 2.Precision: Precision measures the proportion of true positives (correctly identified fraudulent transactions) among all transactions predicted as fraudulent. It is calculated as the ratio of true positives to the sum of true positives and false positives (legitimate transactions incorrectly classified as fraudulent). High precision indicates a low false positive rate, which is desirable in fraud detection to minimize the number of false alarms.Precision = True Positives / (True Positives + False Positives) 3.Recall (Sensitivity): Recall measures the proportion of true positives among all actual fraudulent transactions. It is calculated as the ratio of true positives to the sum of true positives and false negatives (fraudulent transactions incorrectly classified as legitimate). High recall indicates a low false negative rate, ensuring that most fraudulent transactions are detected. Recall = True Positives / (True Positives + False Negatives)
  • 29. Recall could be more important in detecting potentially harmful security threats Finally observed that Logistic regression, decision tree and random forest are the algorithms that gave better results
  • 30. Real-time Implementation and Challenges Implementing fraud detection models in real-time payment processing systems presents several challenges and considerations, including model interpretability, computational efficiency, and handling concept drift Aspects: Model Interpretability: • Challenge: Deep learning models, which are increasingly used in fraud detection, are often regarded as black boxes, making it difficult to understand and interpret their decisions. This lack of interpretability can hinder trust and regulatory compliance
  • 31. Computational Efficiency: • Challenge: Real-time payment processing systems require low-latency responses to handle large transaction volumes. Complex models with high computational requirements may not meet the real-time processing constraints Handling Concept Drift: • Challenge: Concept drift refers to the phenomenon where the statistical properties of the data change over time, leading to degradation in model performance if not addressed. In fraud detection, fraudsters constantly adapt their tactics, causing the fraud detection model's effectiveness to degrade over time
  • 32. Conclusion • In this project, I devised a novel approach for fraud detection. Utilizing the Synthetic Minority Over-sampling Technique (SMOTE), I balanced the dataset, leading to notable improvements in classifier performance. Moreover, I conducted feature scaling to enhance results, and feature selection to eliminate irrelevant features. Employing a range of techniques including Machine Learning, Deep Neural Networks, and Anomaly Detection methods, I observed that Logistic Regression, Decision Trees, Random Forest, and Deep Neural Networks emerged as the top- performing algorithms. • In conclusion, through rigorous experimentation and analysis, it became evident that Logistic Regression, Decision Trees, Random Forest, and Deep Neural Networks exhibit superior performance in detecting fraudulent activities.
  • 33. Discuss potential areas of improvement and future research directions in credit card fraud detection. 1.Enhanced Feature Engineering: Continuous exploration and identification of new features that capture subtle patterns indicative of fraudulent behavior can improve detection accuracy. This may involve incorporating additional transaction metadata, behavioral biometrics, or contextual information associated with transactions. 2.Dynamic Risk Scoring: Developing dynamic risk scoring mechanisms that adapt to evolving fraud patterns in real- time can enhance detection capabilities. Techniques such as adaptive risk scoring models that continuously update risk scores based on recent transaction data and fraud trends can improve fraud detection efficiency. 3.Utilization of Advanced Machine Learning Techniques: Continued exploration and adoption of advanced machine learning techniques, such as ensemble methods, deep learning architectures, and reinforcement learning, can improve fraud detection accuracy, especially in handling complex fraud patterns and large-scale datasets. 4.Unsupervised and Semi-Supervised Learning: Leveraging unsupervised and semi-supervised learning approaches for anomaly detection in credit card transactions can be beneficial, particularly for detecting emerging fraud patterns and previously unseen attacks without relying heavily on labeled data.