FENCE: Fairplay Ensuring Network Chain Entity for Real-Time Multiple ID Detection at Scale In Fantasy Sports
This addresses a specific fraud issue for large-scale fantasy sports platforms like Dream11, which has over 190 million users, but the approach appears incremental as it applies existing graph-based methods to a new domain.
The paper tackles the problem of detecting duplicate or multiple accounts created by users to abuse bonus offers on the Dream11 fantasy sports platform, proposing a graph-based solution that predicts associations between users and identifies clusters of colluding accounts, with a deployed distributed ML system achieving real-time detection to enable corrective actions.
Dream11 takes pride in being a unique platform that enables over 190 million fantasy sports users to demonstrate their skills and connect deeper with their favorite sports. While managing such a scale, one issue we are faced with is duplicate/multiple account creation in the system. This is done by some users with the intent of abusing the platform, typically for bonus offers. The challenge is to detect these multiple accounts before it is too late. We propose a graph-based solution to solve this problem in which we first predict edges/associations between users. Using the edge information we highlight clusters of colluding multiple accounts. In this paper, we talk about our distributed ML system which is deployed to serve and support the inferences from our detection models. The challenge is to do this in real-time in order to take corrective actions. A core part of this setup also involves human-in-the-loop components for validation, feedback, and ground-truth labeling.