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In Business Intelligence, accurate predictive modeling is the key forproviding adaptive decisions. We studied predictive modeling problems in thisresearch which was motivated by real-world cases that Microsoft data scientistsencountered while dealing with e-commerce transaction fraud control decisionsusing transaction streaming data in an uncertain probabilistic decisionenvironment. The values of most online transactions related features can returninstantly, while the true fraud labels only return after a stochastic delay.Using partially mature data directly for predictive modeling in an uncertainprobabilistic decision environment would lead to significant inaccuracy on riskdecision-making. To improve accurate estimation of the probabilistic predictionenvironment, which leads to more accurate predictive modeling, two frameworks,Current Environment Inference (CEI) and Future Environment Inference (FEI), areproposed. These frameworks generated decision environment related featuresusing long-term fully mature and short-term partially mature data, and thevalues of those features were estimated using varies of learning methods,including linear regression, random forest, gradient boosted tree, artificialneural network, and recurrent neural network. Performance tests were conductedusing some e-commerce transaction data from Microsoft. Testing resultssuggested that proposed frameworks significantly improved the accuracy ofdecision environment estimation.

Predictive Modeling with Delayed Information: a Case Study in E-commerce Transaction Fraud Control