Real World examples reducing Fraud through AI and Analytics in the Gaming Industry
Introduction:
OneGame, a gaming AI start-up, has been at the forefront of revolutionizing the gaming industry by leveraging cutting-edge artificial intelligence technology. With a focus on providing a safe and fair gaming experience for its users, OneGame recognized the need to identify risk-based behavior, including frauds committed knowingly, during the user sign-up and login processes. In order to combat these challenges, OneGame embarked on a mission to develop an advanced ML algorithm that could accurately predict such risky behaviors and frauds.
Challenges:
OneGame faced the daunting task of identifying fraudulent activities and risky behaviors exhibited by new users during the sign-up and login process. Traditional methods of manual monitoring and rule-based systems were proving to be ineffective and time-consuming. It was crucial for OneGame to find a solution that could automate this process, provide real-time risk identification, and ensure a secure gaming environment for its users.
Solution:
To address these challenges, OneGame collaborated with industry-leading data scientists and AI experts at Katalyst Stereet to develop a sophisticated ML algorithm capable of predicting risky behaviors and frauds committed by users. The first step was to identify the key variables that contributed to such behaviors and assign appropriate weights to each variable. By leveraging the power of AI, OneGame was able to analyze vast amounts of data and identify patterns that would otherwise go unnoticed.
The ML algorithm developed by OneGame in collaboration with Katalyst Street, incorporated a wide range of variables, including user behavior patterns, device information, IP addresses, and other relevant data points. By assigning appropriate weights to each variable, the algorithm was able to accurately assess the level of risk associated with each user. This allowed OneGame to take proactive measures such as flagging suspicious activities, implementing additional verification steps, or even blocking certain users to ensure a secure gaming environment.
The Impact:
91% Out of Sample Model Prediction Accuracy (True Positives)
The implementation of the ML algorithm developed by Katalyst Street yielded remarkable results. By accurately predicting risky behaviors and flagging potential frauds, OneGame was able to prevent fraudulent activities at the early stages, safeguarding the gaming experience for its users. The real-time risk identification capabilities of the algorithm not only saved time and resources but also enhanced user trust and confidence in OneGame's platform.
Furthermore, the ML algorithm continuously learned from new data, allowing it to adapt and improve its predictive capabilities over time. As a result, OneGame witnessed a significant reduction in fraudulent activities, leading to increased user satisfaction and retention rates. The success of OneGame's AI-powered risk identification solution has positioned the company as a leader in the gaming industry, setting new standards for safety and fairness.
Katalyst Street’s AI saved our game! Their algorithms slashed fraud by 90%, stopped bot attacks, and outwitted evolving threats. We finally built trust with players and secured our resources. Their team is like an extension of ours, always strategizing with us. If you want happy customers and a protected foundation, Katalyst Street is the ultimate weapon against gaming fraud.
-Sachin Bansal, CEO, OneGame Inc
Conclusion:
OneGame's collaboration with data scientists and AI experts has paved the way for a new era in gaming risk identification. By leveraging advanced ML algorithms, OneGame has successfully identified and predicted risky behaviors, providing a safe and secure gaming environment for its users. The implementation of this solution has not only protected users from fraudulent activities but has also enhanced OneGame's reputation as a trusted gaming platform. As the gaming industry continues to evolve, OneGame remains committed to pushing the boundaries of AI technology to ensure an unparalleled gaming experience for all.
Lessons Learned:
Data is a luxury to have: As with any use case involving ML, data is the key. In our case getting access to the user data was a luxury. Infact, we did not had real time data of the user frauds, as the gaming app was launching its application in US for the first time. Our creativity involved the simulation of data attributes, fraud business rules to build our preliminary models.
2. Perfection is the enemy of the good: OneGame is a startup and needed a solution very fast. The speed to a solution also required that we are not perfecting the model, as there will be learnings and iterations to the models as more real time data comes in to play. Our approach was to build an 80% solution with 20% effort and tooling. We saved the perfection of the model towards the later phases of implementation.
3.Explainability and Simplicity trumps complexity: With the availability of the state-of-the-art ML tools and models, the analysts on the project were eager to build the best model with more complexity in implementation and explainability. While we build a sleuth of models, our emphasis has always been towards models that were simple, easy to explain and implement. We were able to explain the trade-offs to the leadership team at OneGame regarding the complexity and real time updates of the weights to a later phase of implementation. With a simple model at 91% accuracy, the OneGame team is more than armed to combat the user fraud for scaling the first 50K users.
4.Implementing AI across Multi-Cloud is a premium: As organizations become more prone to using multiple cloud environments, it presents with unique challenges on accessing AI across cloud ecosystems. We learned that navigating across multi-cloud ecosystems gives us the leverage to implement the best solutions no matter the client preferences.