Credit Card Fraud Detection Using Artificial Neural Network

Credit card fraud prediction is an essential application for financial institutions. To estimate difficult patterns in huge datasets, Artificial Neural Networks (ANNs) are always used for this task with their ability. At, our developers are constantly vigilant when it comes to staying up-to-date with the latest methodologies. We employ cutting-edge algorithms and techniques to ensure precise results and successfully accomplish our research objectives. The following are the processing methods that we implement to construct a credit card fraud detection mechanism using ANNs:

  1. Data Collection:

       The Credit Card Fraud Detection dataset on Kaggle is a generally used dataset which we include in our model. It has many transactions made by credit cards and also contains a small proportion of fraud transactions.  

  1. Pre-processing the Data:
  • Feature Scaling: We utilize Min-Max scaling and standardization to normalize the characteristics, because ANNs are susceptible to feature scaling.
  • Handling Imbalanced Data: Malicious transactions basically occur less often than appropriate ones which lead to class imbalance. We manage this by:
  • Oversampling: Raising the number of minority class examples our project employs SMOTE like methods.
  • Undersampling: Reducing the number of majority class trials.
  • Anomaly Detection: Handling fraud prediction as an abnormality forecasting issue.
  • Dividing the Data: For training, evaluation and validation sets we split the data.
  1. Model Architecture:

Our project develops an easy ANN model using TensorFlow/Keras:


from keras.models import Sequential

from keras.layers import Dense, Dropout

model = Sequential()

# Input layer

model.add(Dense(32, ctivation=’relu’, input_shape=(number_of_features,))) # Hidden layers

model.add(Dense(64, activation=’relu’))


model.add(Dense(32, activation=’relu’))


# Output layer

model.add(Dense(1, activation=’sigmoid’))

  1. Model Compilation & Training:

Since it is a binary classification problem, our model implements binary cross-entropy as the loss function in this project:


model.compile(optimizer=’adam’, loss=’binary_crossentropy’,

metrics=[‘accuracy’]), y_train, epochs=100, batch_size=256,

validation_data=(X_val, y_val), class_weight=class_weights)

Here, class_weights is assisted to allocate more essentiality to the minority class. This is analyzed by us, using techniques like compute_class_weight from scikit-learn.

  1. Evaluation:

We validate the model on the test set by including metrics such as:

  • Precision-Recall curve: Necessary in imbalanced datasets.
  • F1 Score: Harmonic means of precision and recall is useful for us.
  • AUC-ROC: It is known as Area Under the Receiver Operating Characteristic Curve.
  1. Deployment:

When our system’s efficiency gives satisfaction, we apply it as a real-world transaction tracking mechanism and confirm to regularly retrain it with the latest data.


  • Ensemble Methods: To enhance the model’s overall accuracy we examine utilizing methods, integrating detections from various frameworks.
  • Feature Engineering: For improving the framework’s forecasting ability our research constructs the latest features and employs domain skills.
  • Early Stopping: During training our model incorporates early stopping to quit training after the model’s efficiency ends to increase on a held-out test dataset.
  • Regularization: We employ L1 and L2 regularization to prevent overfitting, when the high-dimensional state of transaction data is given.

       Finally, ANN is powerful in predicting credit card fakers, we often remember that costs are related to false positives (legal transactions marked as fraud) and false negatives (malicious transactions marked as legal). It is crucial to consistently track, test and improve our frameworks in such complex applications.

Credit Card Fraud Detection Using Deep Learning

Credit Card Fraud Detection Topics

               A few lists of our thesis ideas and topics that we have worked out recently are listed below, so share with all your research needs we are at your service. A strong thesis statement will be provided for your research. Our thesis writers offer whole range of thesis services right from sharing of ideas and topics, so get all your ANN research work customized as per your needs.

  1. IoT Device Security for Smart Card Fraud Detection for Credit Cards
  2. A Comparison Study of Fraud Detection in Usage of Credit Cards using Machine Learning
  3. Credit Card Fraud Detection using Logistic Regression with Imbalanced Dataset
  4. Extreme Gradient Boost Classifier based Credit Card Fraud Detection Model
  5. Credit Card Fraud Detection Using Machine Learning Techniques
  6. Credit Card Fraud Detection Based on DeepInsight and Deep Learning
  7. Customer behavior-based fraud detection of credit card using a random forest algorithm
  8. Credit Card Fraud Detection: A Hybrid of PSO and K-Means Clustering Unsupervised Approach
  9. Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection
  10. Approx-SMOTE Federated Learning Credit Card Fraud Detection System
  11. Detection of Credit Card Fraud Detection Using HPO with Inception Based Deep Learning Model
  12. Comparative Analysis of Applications of Machine Learning in Credit Card Fraud Detection
  13. Federated Learning-Based Credit Card Fraud Detection: Performance Analysis with Sampling Methods and Deep Learning Algorithms
  14. AMWSPLAdaboost Credit Card Fraud Detection Method Based on Enhanced Base Classifier Diversity
  15. Credit Card Fraud Detection using ML: A Survey
  16. ccfDetector: Utilizing GAN and Deep Learning for Credit Card Fraud Detection
  17. Credit Card Fraud Detection Based on Improved Variational Autoencoder Generative Adversarial Network
  18. A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection
  19. Toward improvement of credit card fraud detection based on Machine learning Techniques
  20. Credit Card Fraud Detection using TabNet
  21. Real-Time Credit Card Fraud Detection Surveillance System
  22. An Intelligent Method for Credit Card Fraud Detection using Improved CNN and Extreme Learning Machine
  23. An Adversarial Learning with Sum of Top-K Loss Framework for Credit Card Fraud Detection
  24. A Novel Framework for Credit Card Fraud Detection
  25. Credit Card Fraud Detection using Neural Embeddings and Radial Basis Network with a novel hybrid fruitfly-fireworks algorithm
  26. Amount and Location Based Credit Card Fraud Detection
  27. Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique
  28. Blockchain and Machine Learning Approaches for Credit Card Fraud Detection
  29. Time-Aware Attention-Based Gated Network for Credit Card Fraud Detection by Extracting Transactional Behaviors
  30. Machine Learning based Credit Card Fraud Detection
  31. A Novel Methodology for Credit Card Fraud Detection using KNN Dependent Machine Learning Methodology
  32. The Hustlee Credit Card Fraud Detection using Machine Learning
  33. Detection of Credit Card Fraud Using Resampling and Boosting Technique
  34. Serverless Stream-Based Processing for Real Time Credit Card Fraud Detection Using Machine Learning
  35. Performance Evaluation of Machine Learning Methods for Detecting Credit Card Fraud
  36. A New GAN-based data augmentation method for Handling Class Imbalance in Credit Card Fraud detection
  37. Fraud Feature Boosting Mechanism and Spiral Oversampling Balancing Technique for Credit Card Fraud Detection
  38. CATCHM: A novel network-based credit card fraud detection method using node representation learning
  39. A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection
  40. The effect of feature extraction and data sampling on credit card fraud detection

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