  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.
