Credit card Fraud Detection using Neural Network
The credit card fraud detection is developing danger field with increasingly more nearness of client's on web. With the presentation of Digital India development, online installments and cash move is expanded. This all raises a gathering of individuals who dupe the online activities.
Credit card fraud detection is a crucial aspect of maintaining the security and integrity of financial transactions. With the advent of technology and the increasing use of online transactions, the risk of credit card fraud has also increased. Traditional methods of detection such as rule-based systems and statistical models have proven to be inadequate in detecting new and sophisticated forms of fraud.
One solution to this problem is the use of neural networks. Neural networks are a type of machine learning algorithm that can be trained to recognize patterns and anomalies in data. They can be used to detect credit card fraud by analyzing transaction data such as the amount, location, and time of the transaction. The neural network can then identify patterns that are indicative of fraudulent activity and flag them for further investigation.
One of the benefits of using neural networks for credit card fraud detection is that they can learn and adapt to new patterns of fraud. As fraudsters come up with new methods of deception, the neural network can be retrained to recognize these new patterns. This makes it more difficult for fraudsters to evade detection.
Another advantage of neural networks is their ability to process large amounts of data quickly and accurately. This is particularly useful for identifying fraud in real-time, as it allows for immediate action to be taken to prevent the loss of funds.
In conclusion, neural networks are a powerful tool for credit card fraud detection. They can learn and adapt to new patterns of fraud, process large amounts of data quickly and accurately, and detect fraudulent activity in real-time. This makes them a valuable asset in the fight against credit card fraud.
Methodology
The major aspect of this project to develop a best-suited algorithm to find the outliers or frauds in the case of credit cards. We will implement several machine learning and deep learning algorithms and compare them and choose the best algorithm.
We will implement algorithms like:
- Neural Network
- Isolation Forest
- OneClassSVM
- Local Outlier Factor
- DBSCAN
For this purpose, we used an existing dataset. The dataset contains information about transactions made by various cardholders. The dataset composed of around 300,000 records out of which there are only around only 500 fraudsters. So, this shows that the dataset is highly imbalanced as the positive class or frauds are only 0.172% of all transactions. All the features columns are numeric which is the result of PCA transformation. Hence their value ranges from -1 to 1. Features columns V1, V2, V3… V28 are obtained as a result of PCA transformation. Columns like Time and Amount have not been transformed. Feature Class column is the classification variable which contains value 0 (Normal Case) and 1 (Fraud).