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

Machine Learning;Computers and Society;Machine Learning