Fraud-Detection

Fraud is a serious problem that affects businesses and individuals alike. With the increasing amount of digital transactions and online activity, the risk of fraud has also increased. Fraud detection has become a critical aspect of financial services, insurance companies, and e-commerce platforms, among others.

The aim of this project is to develop a fraud detection system using machine learning techniques. The system will analyze transactional data and identify patterns that are indicative of fraudulent activity. We will use a combination of supervised and unsupervised learning techniques to train the model and optimize its performance.

The project will involve data preprocessing, feature engineering, model selection, and hyperparameter tuning. We will also evaluate the performance of the model using metrics such as precision, recall, and F1 score.

1 Introduction and preparing data

1.1 Data visualization

1.2 Increase successful detections with data resampling

1.3 Fraud detection algorithms

2 Fraud detection using labeled data

2.1 Review classification methods

2.2 Perfomance evaluation

2.3 Adjusting the algorithm weights

2.4 Ensemble methods

3 Fraud detection using unlabeled data

3.1 Normal versus abnormal behavior

3.2 Clustering methods to detect fraud

3.3 Assigning fraud vs. non-fraud

3.4 Alternate clustering methods for fraud detection

4 Fraud detection using text

4.1 Using text data

4.2 Text mining to detect fraud

4.3 Topic modeling on fraud

4.4 Flagging fraud based on topic