AI Alert Dispositioning for False Positive Reduction
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Solution Overview
Problem
Existing systems for generating risk-related alerts in customer service environments suffer from high volumes of false-positive alerts, leading to time inefficiencies as supervision principals manually review and close these alerts, which are often non-issues.
Innovation Solution
A machine learning-based alert triaging system that uses self-supervised ML, NLP, and AI-driven text classification, combined with customer demographic and risk metrics, to automatically close non-issue alerts by analyzing unstructured conversation threads from CRM and RMS platforms, reducing false positives through clustering and relevance scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of all alerts is performed, then alert accuracy is improved, but time efficiency deteriorates
Solution Approach 1:
The system enables self-service by implementing an automated alert triaging mechanism that independently evaluates and disposes of alerts without requiring manual review for every case. The machine learning model automatically determines whether alerts are false positives or require escalation, allowing the system to serve itself in filtering alerts while maintaining high accuracy through continuous learning from supervision principal decisions.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between alert generation and manual review. This intermediary component analyzes alert characteristics, compares them against historical patterns, and predicts whether alerts are likely false positives, thereby reducing the volume of alerts requiring human review while preserving accuracy for genuine issues.
2Productivity
If automated alert closure is implemented, then time efficiency is improved, but alert accuracy deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on historical alert data and supervision principal decisions before deployment. This preliminary training phase allows the model to learn patterns of false positives and genuine alerts, establishing accurate decision-making capabilities before automated closure begins, thereby maintaining high accuracy while improving productivity.
Solution Approach 2:
The patent implements feedback mechanisms where supervision principals' manual review decisions are fed back into the training dataset, continuously improving the model's accuracy. The system learns from correct and incorrect automated dispositions, adjusting its predictions to reduce false positives while maintaining sensitivity to genuine risks, thus improving both accuracy and productivity over time.
3Measurement precision
If large volumes of alerts are reviewed manually, then comprehensive analysis is improved, but productivity deteriorates
Solution Approach 1:
The system applies taking out by extracting and analyzing only the most relevant features and patterns from alert data using machine learning. Instead of requiring comprehensive manual review of all alert details, the model extracts key discriminative features that indicate false positives versus genuine alerts, enabling comprehensive analysis of critical aspects while dramatically improving productivity by automating the filtering process.
4Reliability
If more alerts are generated for safety, then risk coverage is improved, but false positives increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting alert generation thresholds and model decision parameters based on learned patterns. The system can modify sensitivity parameters to balance risk coverage and false positive rates, allowing comprehensive risk monitoring while reducing spurious alerts through data-driven parameter optimization rather than fixed thresholds.
Data Source
AI summary
The system identifies false positives about a user and closes resulting alerts before the alert wastefully consume system resources. Machine learning techniques including clustering and multi-labeling classification are used to effectively categorize prior text-based notes to efficiently identify and automatically close false positive alerts. Moreover, weak labeling, AI transformers/sentence transformation, and/or k-means cluster analysis provide a means for condensing large quantities of textual data into ML model components with improved interpretability. Customer relationship management (CRM) platform and Risk Management Supervision (RMS) note analysis captures inefficiently/ineffectively organized past work and leverages it to reduce redundancies in future expert user/supervisory/customer advisory efforts. The various ML techniques disclosed herein output numerical features that directly improve model performance for alert triaging as a whole. Streamlining the presentation of large textual data to reviewers/users (e.g., supervision principals) as individual sentences or numerical values greatly improves explainability as a byproduct.