Google COLAB - CREDIT CARD FRAUD DETECTION
ABSTARCT :
Credit card fraud is currently the most common problem in the modern world. This is because internet transactions and e-commerce sites are on the rise. Credit card fraud occurs when a credit card is stolen and used for unauthorized reasons, or when a fraudster exploits the credit card information for his own interests.
In today's environment, we're dealing with a bunch of credit or debit card issues. Despite the fact that the financial industry is filled with criminal activity, credit card theft is the most common and concerning to online clients. A credit card fraud detection system was implemented to detect fraudulent actions.
The major goal of this initiative is to focus on fraud transaction prevention. The ML algorithm determines whether or not the transaction is valid. This is accomplished by using the provided dataset to learn. Random forest is the algorithm that were used.
We built a website where you can register your details for login and submit card information for registration, for further security. The algorithms will then examine and identify the transactions.
To prevent fraud by exploiting the authorized person's photo, the facial recognition detection uses Haar features and a cascade classifier.
EXISTING SYSTEM :
In the current approach, fraud is recognized after the fraud has been committed, that is, after the holder has filed a complaint. As a result, the cardholder experienced a lot of difficulties before the investigation was completed.
Also, because all transactions are kept in a log, we need to save a lot of data, and because a lot of purchases are made online these days, we don't know who is using the card online, so we only collect their IP address for verification.
As a result, cybercrime assistance is required to investigate the scam. To eliminate all of the abovementioned drawbacks, we suggest a system for quickly detecting fraud. There is no mechanism to prevent fraudulent transactions.
DISADVANTAGE :
Limited RAM/CPU: Google Colab offers free access to GPUs, but the available resources (RAM and CPU) can be limited. For large datasets or complex machine learning models, the platform may not be sufficient, leading to slow processing times or out-of-memory errors.
Sensitive Data Handling: Credit card fraud detection involves sensitive personal information. Uploading this data to Google Colab can be a concern from a privacy and security standpoint, especially if you are working with unencrypted data or using public datasets.
Model Training Speed: While GPUs are available, the free version of Colab offers limited access to more powerful hardware. For large-scale models like deep neural networks, training could take longer, potentially leading to inefficient workflows.
Internet Connection Requirement: Google Colab relies heavily on a stable internet connection. If your internet is slow or unreliable, it can affect your ability to use the platform efficiently for real-time model training or data processing.
PROPOSED SYSTEM :
This suggested method is designed to evaluate whether a certain transaction is genuine or not. The random forest ML algorithm is used to detect the fraudulent transactions. The efficiency, accuracy, recall, and F1-score of the two techniques are used to compare their outcomes.
The confusion matrix is used to plot the ROC curve. When the Random Forest method is evaluated, this algorithm is determined to be the best for detecting fraud.The Hidden Markov model is based on the user's spending habits.
User is assigned to one of three categories: low, medium, or high. Facial recognition is now available in Python and OpenCV. This adds another layer of security to the user's credentials. The detection of fraudulent card use is more faster than the current system.
Every transaction includes face authentication for the original cardholder. As a result, the log is kept for fraud detection. The log, which will be kept, will also serve as proof of the transaction to the bank. Using this method, we may get a most precise detection. This cuts down on a bank employee's boring duties.
ADVANTAGE :
Free GPU/TPU Access: Google Colab provides free access to powerful hardware like Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). These resources accelerate machine learning tasks such as training deep learning models, making it faster to build and test credit card fraud detection systems.
No Local Installation: Colab is entirely cloud-based, so you don’t need to worry about setting up complex environments or installing libraries on your own machine. You can start coding right away without needing additional software or hardware setup.
Easy Collaboration: Colab allows easy sharing and collaboration with others, similar to Google Docs. Multiple users can work on the same notebook simultaneously, making it convenient for team-based fraud detection model development.
Easy Data Storage: Colab integrates directly with Google Drive, making it easy to store and retrieve datasets. You can also use Google Drive as a persistent storage solution, saving models, intermediate results, and datasets for long-term access.
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