There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and fraud detection system to resolve this limitation. The system leverages three deep learning models: autoencoder (AE), variational autoencoder (VAE), and a generative adversarial network. We preprocess the given dataset to separate the Transaction Date attribute into its base components to capture time-related fraud patterns. Also, Wasserstein Generative Adversarial Network (WGAN) is used to generate fraud transactions, which are then mixed with the base dataset to form a more balanced mixed dataset. These two datasets are used to train the AE and VAE models. We built two versions of the AE model (single-loss and multi-loss) besides a novel method of calculating the anomaly score threshold, called Recall-First Threshold (RFT), which helps enhance the model's performance. Experimental results demonstrated that the False Positive Rate (FPR) drops down to as low as 7% in the proposed multi-loss AE model. In comparison, we achieved an accuracy of 93%, with 100% of the fraud transactions recalled successfully.

Jatropha curcas L. is one of the recently planted trees that utilizes wastewater in Egypt. It is not just because of its features, such as drought tolerance, rapid growth, and easy propagation, higher oil content than other oil crops, but also because of the Egyptian unique model which uses wastewater for planting Jatropha in the marginal desert land, which in turn represents an excellent opportunity to make use of such land. Moreover, this system provides a good way for reusing the treated sewage water, which itself represents an environment hazard. In addition, Jatropha plantations can be used in the future to be the base for the biodiesel production industry. This review tried to cover the current situation of Jatropha plantations in Egypt. To do so, the paper first reviewed the available land and wastewater resources, and then the potential EU biofuel market situation. Finally, it discussed the biofuel production potentials in Egypt.