SOAR — Sparse Oracle-based Adaptive Rule extraction: Knowledge extraction from large-scale datasets to detect credit card fraud
Nick F Ryman-Tubb, Artur d’Avila Garcez · 2010
This paper presents a novel approach to knowledge extraction from large-scale datasets using a neural network when applied to the real-world problem of payment card fraud detection. Fraud is a serious and long term threat to a peaceful and democratic society. We present SOAR (Sparse Oracle-based Adaptive Rule) extraction, a practical approach to process large datasets and extract key generalizing rules that are comprehensible using a trained neural network as an oracle to locate key decision boundaries. Experimental results indicate a high level of rule comprehensibility with an acceptable level of accuracy can be achieved. The SOAR extraction outperformed the best decision tree induction method and produced over 10 times fewer rules aiding comprehensibility. Moreover, the extracted rules discovered fraud facts of key interest to industry fraud analysts.