Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions
ABSTARCT :
Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in large datasets, such as credit card transactions.
However, most credit card fraud models in the literature are based on traditional ML algorithms, and recently, there has been a rise in applications based on deep learning techniques.
This study reviews the recent DL-based literature and presents a concise description and performance comparison of the widely used DL techniques, including convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU).
Additionally, an attempt is made to discuss suitable performance metrics, common challenges encountered when training credit card fraud models using DL architectures and potential solutions, which are lacking in previous studies and would benefit deep learning researchers and practitioners.
Meanwhile, the experimental results and analysis using a real-world dataset indicate the robustness of the deep learning architectures in credit card fraud detection.
EXISTING SYSTEM :
In case when the output variable is in the form of a specific category, the classification method is considered. Whereas, when the output is achieved as a real value like dollars or weight, a regression method is followed.
The unsupervised learning approach is less efficient as compared to supervised learning approach. In the unsupervised learning approach the labels are not assigned to training set [7].
The technique needs to apply which assign label to the training set to get final result of classification. Grouping the unsorted type of data on the basis of the patterns, similarities and differences without performing any prior training on it, is the main function of unsupervised learning approach.
In the unlabeled data, the hidden structure is to be identified by the machine on its own. Clustering and association are the two categories in which the unsupervised algorithms are classified.
DISADVANTAGE :
Complexity and Interpretability:Deep learning models, particularly deep neural networks (DNNs) and convolutional neural networks (CNNs), are often considered "black boxes." This means that it can be very difficult to understand how the model arrived at a specific decision.
Training Data Requirements:Deep learning models require large amounts of labeled data to perform well. Acquiring a sufficiently large dataset of fraudulent transactions can be difficult due to privacy concerns and the fact that fraud patterns evolve continuously.
High Computational Cost:Training deep learning models is computationally expensive, requiring high-performance hardware (such as GPUs) and a significant amount of time.
Overfitting:Deep learning models, especially when trained on a limited or noisy dataset, are prone to overfitting, where they memorize the training data rather than learning generalizable patterns.
PROPOSED SYSTEM :
The system incorporates several deep learning models, including Feedforward Neural Networks (FNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, to analyze transaction sequences and identify anomalous patterns indicative of fraud.
To address data imbalance, the system employs techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and cost-sensitive learning, ensuring that the model remains sensitive to rare fraudulent transactions.
Additionally, the system uses autoencoders for anomaly detection, allowing for the identification of outliers without requiring labeled fraudulent data.
To improve real-time fraud detection, the system integrates lightweight neural network architectures and model optimization techniques, ensuring low-latency predictions suitable for production environments.
ADVANTAGE :
High Accuracy and Detection Power: Deep learning models, particularly deep neural networks (DNNs) and recurrent neural networks (RNNs), can learn intricate patterns and complex relationships in transaction data that traditional machine learning methods might miss.
Ability to Handle Large and Complex Datasets: Credit card fraud detection involves large volumes of data with multiple variables, such as transaction amounts, time, location, and user behavior. Deep learning is particularly suited to handle and process such large datasets, including unstructured data.
Automated Feature Extraction: Unlike traditional methods that require extensive feature engineering, deep learning models can automatically learn relevant features from raw data, such as transaction sequences or temporal patterns.
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