Predictive Friction Points for Real-Time Fraud Prevention
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Solution Overview
Problem
Online marketplaces face challenges in real-time fraud detection, with existing solutions being primarily reactive, allowing fraudsters to operate before detection, and resulting in potential losses due to delayed identification of fraudulent activities.
Innovation Solution
A predictive fraud prevention system that uses machine learning models to evaluate user data and dynamically implement friction points, such as additional verification steps or restrictions, to slow down or prevent fraudulent behavior, thereby providing a proactive approach to fraud detection and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive fraud detection is used, then existing fraud can be identified, but fraudsters have a head-start and time to continue scams
Solution Approach 1:
The system performs preliminary actions by predicting fraudulent behavior before it occurs. Machine learning models analyze user data, device information, and behavioral patterns to identify potential fraudsters in advance, allowing the system to take preventive measures before actual fraudulent transactions happen. This transforms the detection approach from reactive to proactive, eliminating the time head-start that fraudsters currently have.
2Reliability
If friction points are implemented to slow down fraudulent behavior, then fraud prevention improves, but legitimate user experience may be affected
Solution Approach 1:
The system applies local quality by implementing friction points selectively only where needed. Instead of applying uniform restrictions to all users, the machine learning model identifies specific users with high fraud risk and applies additional verification steps or restrictions only to them. Legitimate users continue to experience the platform normally, while only suspected fraudsters encounter the additional friction, thus maintaining ease of operation for most users while preventing fraud.
Solution Approach 2:
The system changes parameters dynamically based on risk assessment. The friction service adjusts verification requirements, transaction limits, and access levels as parameters based on the predicted fraud likelihood of each user. High-risk users face increased friction parameters (more verification steps, lower transaction limits), while low-risk users experience standard parameters, allowing the system to adapt the user experience to individual risk profiles.
3Reliability
If additional verification steps are added to prevent fraud, then security improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary friction service that sits between the user and the core platform functionality. This intermediary layer handles all verification logic, friction point implementation, and coordination with machine learning models. By centralizing verification complexity in a dedicated service layer, the core platform remains simple while the friction service absorbs the complexity of additional verification steps, making the system modular and easier to manage.
Data Source
AI summary
Provided are systems and methods for inserting friction points into a user session with an online resource such as a website or mobile application. In one example, the method may include receiving user data, predicting a likelihood that the user will behave inappropriately when interacting with an online resource of the host platform based on the received user data, determining a friction point in response to the predicted likelihood the user will behave inappropriately being greater than a predetermined threshold, and modifying at least one of displayed content and interactive capabilities of the online resource which are available to the user based on the determined friction point.