ML Fraud Reduction via Dynamic Incentives
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Solution Overview
Problem
Payment services face challenges in differentiating between legitimate and fraudulent user accounts, especially during the onboarding process when minimal user data is available, leading to vulnerabilities in fraud detection and prevention.
Innovation Solution
The implementation of a machine learning model that analyzes user data, including phone numbers and email addresses, to determine a risk metric and dynamically generate personalized incentives, thereby mitigating fraudulent behavior by disincentivizing high-risk users and incentivizing legitimate ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods are used during onboarding, then system simplicity is maintained, but fraud detection accuracy deteriorates due to minimal user data availability
Solution Approach 1:
The system performs preliminary fraud risk assessment during the onboarding process by analyzing user data as it becomes available. The machine learning model continuously evaluates risk metrics before fraudulent activities can occur, enabling early intervention while maintaining a relatively simple system architecture.
Solution Approach 2:
A machine learning model acts as an intermediary between the minimal user data available and the fraud detection decision. This intermediary processes and interprets the limited data through learned patterns, achieving high detection accuracy without requiring complex manual verification systems.
2Reliability
If uniform incentives are provided to all users, then implementation simplicity is maintained, but fraud prevention effectiveness deteriorates due to inability to differentiate user risk levels
Solution Approach 1:
The incentive system applies different incentive levels to different user segments based on their individual risk metrics. Low-risk users receive higher incentives to encourage legitimate engagement, while high-risk users receive reduced or no incentives, creating a localized, targeted approach that improves fraud prevention effectiveness.
Solution Approach 2:
The system dynamically changes the incentive parameter based on the user's risk metric. The incentive amount is adjusted as a function of the risk assessment, allowing the system to adaptively respond to different user profiles without requiring complex manual intervention.
3Productivity
If high incentives are offered to encourage user registration, then user acquisition speed increases, but fraud vulnerability worsens due to attraction of fraudulent accounts
Solution Approach 1:
The system applies preliminary anti-action by reducing or eliminating incentives for high-risk users before they can exploit the incentive program for fraudulent purposes. This preemptive measure blocks fraudulent account creation while maintaining attractive incentives for legitimate users, thus protecting against harm while preserving productivity.
Solution Approach 2:
The incentive structure is made dynamic rather than static. Incentives automatically adjust based on real-time risk assessment, allowing the system to quickly respond to emerging fraud patterns while maintaining high user acquisition rates for legitimate accounts.
4Measurement precision
If minimal user data is collected during onboarding, then user privacy and onboarding speed are maintained, but fraud detection capability deteriorates
Solution Approach 1:
The system replaces traditional mechanical fraud detection methods that rely on large volumes of user data with a machine learning-based approach. The ML model substitutes for extensive data collection by extracting meaningful fraud signals from minimal data through learned patterns and relationships.
Solution Approach 2:
The machine learning model serves as an intermediary that enhances the information content of minimal user data. It processes limited input data and generates comprehensive risk assessments, effectively amplifying the fraud detection capability without requiring proportional increases in data collection.
Data Source
AI summary
Using a machine learning model(s) for fraud reduction is described. A payment service computing platform may receive, from an electronic device, user data associated with a user, and dynamically determine an incentive(s) associated with the user based on the user data. The incentive(s) may be determined using a trained machine learning model(s) that is trained based on previously collected user data. The payment service computing platform can then cause a user interface to be displayed via a payment application executing on the electronic device, wherein the user interface presents an interactive element(s) for receiving the incentive(s) in exchange for the user referring at least one other user to a payment service.