Credit card fraud detection solutions
- 1. Credit Card Fraud Detection Solutions – How to Implement Them in Your Business
- 6. Credit Card Fraud Detection with Machine Learning is a process of data investigation by a Data Science team and the development of a model that will provide the best results in revealing and preventing fraudulent transactions. This is achieved through bringing together all meaningful features of card users’ transactions, such as Date, User Zone, Product Category, Amount, Provider, Client’s Behavioral Patterns, etc. The information is then run through a subtly trained model that finds patterns and rules so that it can classify whether a transaction is fraudulent or is legitimate. Now you know what fraud protection is, let’s look at the most common types of threats.
- 7. AI Powered Fraud Detection System. Implementation Steps ● Data Mining. Implies classifying, grouping, and segmenting of data to search millions of transactions to find patterns and detect fraud. ● Pattern Recognition. Implies detecting the classes, clusters, and patterns of suspicious behavior. Machine Learning here represents the choice of a model/set of models that best fit a certain business problem. For example, the neural networks approach helps automatically identify the characteristics most often found in fraudulent transactions; this method is most effective if you have a lot of transaction samples.
- 8. Advanced Credit Card Fraud Identification Methods are split into: ● Unsupervised. Such as PCA, LOF, One-class SVM, and Isolation Forest. ● Supervised. Such as Decision Trees (e.g. XGBoost and LightGBM), Random Forest, and KNN. We’ve covered the basic vision of how Machine Learning for fraud detection works. Let’s now dig deeper into the exact models that make it possible.
- 9. For more information check out the full article https://spd.group/machine-learning/credit-card-fraud-detection/