Credit Card Fraud Detection.

What is Credit Card Fraud?

Credit card fraud is when someone uses another person’s credit card or account information to make unauthorized purchases or access funds through cash advances. Credit card fraud doesn’t just happen online; it happens in brick-and-mortar stores, too. As a business owner, you can avoid serious headaches — and unwanted publicity — by recognizing potentially fraudulent use of credit cards in your payment environment

What is machine learning?

Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.

How Machine Learning is used in Credit Card Fraud Detection?

The first and foremost step involves collecting and sorting raw data, which is then used to train the model to predict the probability of fraud. The solutions offered by machine learning for credit card fraudulent detection involve:

1. Classifying whether credit card transactions are authentic or fraudulent using algorithms such as logistic regression, random forests, support vector machines (SVMs), deep neural networks along with autoencoders, long short-term memory (LSTM) networks, and convolutional neural networks (CNNs).
2. Predicting whether it is the cardholders or the fraudsters using the credit cards through credit card profiling.
3. Using outlier detection methods to identify considerably different transactions (or ‘outliers’) from regular credit cards transactions to detect credit card fraud.

Okay all good till now!

Now, what is Logistic Regression Algorithm and how it is used?

• Logistic regression is a Machine Learning classification algorithm that is used to predict the probability of certain classes based on some dependent variables.
• In short, the logistic regression model computes a sum of the input features and calculates the logistic of the result.
• The output of logistic regression is always between (0, and 1), which is suitable for a binary classification task.
• The higher the value, the higher the probability that the current sample is classified as class=1, and vice versa.

Conclusion:

Fraud detection system have become essential for banks and financial institution, to minimize their losses.

However, there is a lack of published literature on credit card fraud detection techniques, due to the unavailable credit card transactions dataset for researchers.

Credits: SHREYA MHETRE & Deven Nandapurkar

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