ML Disclaimer Model for Automated Message Compliance Filtering
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Solution Overview
Problem
Organizations face challenges in distinguishing between trusted and malicious electronic communications, leading to inefficiencies in message compliance scanning and processing, particularly in balancing efficiency and resource consumption.
Innovation Solution
A computing platform is used to preprocess historical messages, train a disclaimer model to identify messages with disclaimers, and a low risk content model to categorize messages as newsletters or automated notifications, thereby filtering out messages that require further compliance review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all electronic messages are reviewed for compliance before routing to recipients, then message security and compliance are improved, but processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary classification of messages using machine learning models before full compliance review. Messages are pre-processed to identify obvious compliant patterns, allowing them to be routed quickly without exhaustive analysis. This preliminary action filters out low-risk messages early in the process.
Solution Approach 2:
The compliance review process is segmented into multiple stages: initial ML-based classification, risk scoring, and detailed compliance analysis only for flagged messages. This segmentation allows the system to apply different levels of scrutiny to different message types, reducing overall processing time while maintaining compliance standards.
2Measurement precision
If comprehensive compliance analysis is performed on all messages, then compliance accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system applies partial compliance analysis to all messages through ML classification, and excessive/detailed analysis only to messages that exceed a risk threshold. This partial action approach ensures high accuracy for risky messages while conserving resources on obviously compliant messages.
Solution Approach 2:
Machine learning models serve as intermediaries between incoming messages and the full compliance analysis system. These intermediaries pre-screen messages and filter out low-risk ones, allowing the expensive compliance analysis resources to be focused only on messages that truly need detailed review.
3Reliability
If manual review processes are used for all messages, then compliance thoroughness is improved, but processing efficiency and speed decrease
Solution Approach 1:
The system enables self-service compliance verification through automated ML classification and risk scoring. Messages that clearly meet compliance criteria based on their content and metadata are automatically approved without human intervention, allowing the system to handle high volumes while maintaining thoroughness for suspicious messages.
Solution Approach 2:
The review process dynamically adjusts based on message characteristics. High-risk messages receive full manual review, while low-risk messages receive automated approval. This dynamic approach optimizes the balance between thoroughness and efficiency for each individual message based on its risk profile.
Data Source
AI summary
Aspects of the disclosure relate to message compliance analysis. A computing platform accesses historical messages. The computing platform pre-processes the historical messages to configure the historical messages for use in training a disclaimer model to identify whether or not input messages include a disclaimer. The computing platform trains, using the pre-processed historical messages, the disclaimer model. The computing platform receives a new message. The computing platform inputs, into the disclaimer model, the new message, which produces a disclaimer score indicating a likelihood that the new message includes a disclaimer. The computing platform compares the disclaimer score to a disclaimer threshold. Based on identifying that the disclaimer score meets or exceeds the disclaimer threshold, the computing platform removes, from a set of messages scheduled for compliance review, the new message, and sends, to an intended recipient of the new message, the new message.