Machine Learning User Classification for Selective Risk Intervention
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Solution Overview
Problem
Businesses face challenges in managing risk exposure by accurately assessing the trustworthiness of customers and minimizing the cost of interventions to mitigate risks, which can degrade the customer experience and impact relationships with well-intentioned customers.
Innovation Solution
A system and method for computing a trusted user metric using machine learning to evaluate customer trustworthiness and selectively apply interventions based on statistical models, optimizing the accuracy of risk assessment while minimizing customer disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If businesses apply interventions to mitigate risks, then risk management effectiveness is improved, but customer experience deteriorates due to disruption
Solution Approach 1:
The system applies different levels of intervention to different customers based on their individual risk profiles. High-risk customers receive more intensive monitoring and interventions, while low-risk customers experience minimal disruption. This localized approach ensures risk management effectiveness is maintained for vulnerable customers while preserving customer experience for well-intentioned ones.
Solution Approach 2:
The machine learning model dynamically adjusts intervention thresholds and parameters based on changing risk assessments. As customer behavior patterns evolve and risk scores change, the system modifies the intensity and frequency of interventions accordingly, optimizing the balance between risk mitigation and customer experience over time.
2Measurement precision
If businesses increase intervention intensity to improve risk assessment accuracy, then measurement precision is improved, but loss of time increases due to additional verification steps
Solution Approach 1:
The system applies verification actions selectively rather than universally. Based on the machine learning risk assessment, only customers who cross certain risk thresholds undergo additional verification steps. This partial action approach maintains high measurement precision for risky cases while avoiding time loss for low-risk customers who don't require extensive verification.
Solution Approach 2:
The system performs preliminary risk assessment using available data before initiating time-consuming verification processes. By pre-evaluating customer risk profiles using machine learning models on existing transaction and behavioral data, the system identifies which customers actually need additional verification, thereby minimizing overall verification time while maintaining assessment accuracy.
Data Source
AI summary
Systems and methods for classifying a user and issuing actions are disclosed. One method may include receiving a first score for a first characteristic associated with a user and a second score for a second characteristic associated with the user. The first and second scores may be evaluated for determining a first metric for the user. A criterion may be detected for reevaluating the first metric. Based on detecting the criterion, a first action and a timing of the first action may be selected for obtaining information associated with the user. The first action and timing of the first action may be configured to maximize accuracy of a prediction of a second metric and minimize a cost associated with the first action. The second metric may be generated based on the information obtained via the first action. A second action may be performed based on the second metric.