Social gaming platforms face ongoing threats from fraud and cheating, which can undermine trust and the player experience, and machine learning algorithms now play a key role in identifying and addressing these risks. Types of fraudulent actions include account farming, where individuals or automated bots create multiple accounts to exploit bonuses or climb social rankings; bonus abuse, where players coordinate to manipulate rewards; multi-accounting; collusion; device sharing; identity risks involving forged or stolen payment credentials; payment fraud schemes, such as chargebacks after receiving virtual goods; automated bot networks that complete repetitive tasks at superhuman speeds; and credential stuffing attacks using stolen login information from data breaches. Effective detection systems rely on robust, multifaceted data, including behavioral telemetry such as session length, timing, and in-game actions, as well as device fingerprints like unique browser setups, operating systems, and IP addresses.