Predictive Analytic as a Service on Tax Evasion Using Feature Engineering Strategies

Abstract

Predictive analytics can forecast trends and determines statistical probabilities and to act upon fraud and security threats for big data applications such as business trading, fraud detection, crime investigation, banking, insurance, enterprise security, government, healthcare, e-commerce, and telecommunications. Predictive analytics as a service (PAaaS) framework is proposed in our earlier work. One solution based upon ensemble model that uses Gaussian process with varying hyper-parameters is also given in our earlier works. Test results proved that the third hyper-parameter values yielded a good result with less error rate and more variance which is reliable for a predictive model. This paper presents solution based upon ensemble model that uses best out of prediction algorithms such as artificial neural networks (ANN), auto-regression algorithm (ARX) and Gaussian process (GP). Feature engineering methods such as recursive feature elimination that uses random forest algorithm is used for attribute selection. Performance measures NRMSE and COD are used to analyze the model. Test results proved that neural networks performed well when compared to regression and Gaussian process.