An adaptive predictive financial fraud detection approach using deep learning methods on a Big Data platform
Thesis
Fraud, waste, and abuse in many financial systems are estimated to result in significant losses annually. Predictive analytics offer government and private financial institutions the opportunity to identify, prevent or recover such losses. This work proposed a novel Big Data driven approach for fraud detection based on Deep Learning methods. A supervised Deep Learning solution leveraging Big Data was shown to be an effective Fraud predictor. Additionally, an unsupervised method based on anomaly detection using deep autoencoders was proposed for when there is few or no labelled data. The two methods presented offered adaptive and predictive Fraud detection through improved Analytics. Future work will look into how the two methods can be integrated into an effective tool for enhanced Fraud detection.