Parallel granular neural networks for fast credit card fraud detection

Citations Over TimeTop 10% of 2003 papers

Abstract

A parallel granular neural network (GNN) is developed to speed up data mining and knowledge discovery process for credit card fraud detection. The entire system is parallelized on the Silicon Graphics Origin 2000, which is a shared memory multiprocessor system consisting of 24-CPU, 4G main memory, and 200 GB hard-drive. In simulations, the parallel fuzzy neural network running on a 24-processor system is trained in parallel using training data sets, and then the trained parallel fuzzy neural network discovers fuzzy rules for future prediction. A parallel learning algorithm is implemented in C. The data are extracted into a flat file from an SQL server database containing sample Visa Card transactions and then preprocessed for applying in fraud detection. The data are classified into three categories: first for training, second for prediction, and third for fraud detection. After learning from training data, the GNN is used to predict on a second set of data and later the third set of data is applied for fraud detection. GNN gives fewer average training errors with larger amount of past training data. The higher the fraud detection error is, the greater the possibility of that transaction being actually fraudulent.

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