Discriminative Data-driven Self-adaptive Fraud Control Decision System with Incomplete Information

TL;DR

This paper introduces a novel, data-driven, self-adaptive fraud control system for online transactions that considers the dynamic interactions among multiple decision parties, improving profit through real-time strategy adjustments.

Contribution

It proposes a new fraud control framework that models interactive decision effects and employs AI and optimization for real-time strategy adaptation, a novel approach in the field.

Findings

Significant profit improvement in Microsoft online transaction data

Effective modeling of multi-party decision interactions

Real-time adaptive fraud control strategies

Abstract

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.