FENCE: Fairplay Ensuring Network Chain Entity for Real-Time Multiple ID Detection at Scale In Fantasy Sports

TL;DR

This paper presents FENCE, a scalable, real-time graph-based system for detecting multiple accounts in fantasy sports platforms, combining machine learning and human validation to prevent abuse and ensure fair play.

Contribution

Introducing a distributed ML system with graph-based modeling and human-in-the-loop for real-time detection of duplicate accounts in large-scale fantasy sports.

Findings

Effective detection of colluding accounts in real-time

Scalable system supporting over 190 million users

Integration of machine learning with human validation

Abstract

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.