Discriminative Data-driven Self-adaptive Fraud Control Decision System
  with Incomplete Information
  While E-commerce has been growing explosively and online shopping has become
popular and even dominant in the present era, online transaction fraud control
has drawn considerable attention in business practice and academic research.
Conventional fraud control considers mainly the interactions of two major
involved decision parties, i.e. merchants and fraudsters, to make fraud
classification decision without paying much attention to dynamic looping effect
arose from the decisions made by other profit-related parties. This paper
proposes a novel fraud control framework that can quantify interactive effects
of decisions made by different parties and can adjust fraud control strategies
using data analytics, artificial intelligence, and dynamic optimization
techniques. Three control models, Naive, Myopic and Prospective Controls, were
developed based on the availability of data attributes and levels of label
maturity. The proposed models are purely data-driven and self-adaptive in a
real-time manner. The field test on Microsoft real online transaction data
suggested that new systems could sizably improve the company's profit.
