Compliance Scenario Prediction Model for Digital Forms
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Solution Overview
Problem
Existing digital form filing systems require users to manually answer numerous questions in an interview-based workflow, making the compliance rule-based topic qualification process time-consuming and inefficient, especially in online live systems where expert intervention is needed.
Innovation Solution
A machine learning-based scenario prediction system that combines user features and completeness paths within a compliance graph to predict user cohorts and infer personalized responses, reducing the number of questions presented to users and automating the decision-making process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually answer questions in an interview-based workflow to determine compliance scenarios, then the system can accurately identify the correct scenario, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary analysis by evaluating user-provided data against compliance rules before the interview workflow begins. The completeness graph pre-calculates which scenarios are potentially applicable based on existing data, so that during the interview, only relevant questions need to be asked rather than all possible questions, significantly reducing time while maintaining accuracy.
2Reliability
If the system asks all questions in the completeness graph to ensure complete topic qualification, then accuracy is maintained, but the number of questions increases and user experience deteriorates
Solution Approach 1:
The system extracts and identifies only the essential questions needed to determine compliance scenarios based on the user's specific situation. By analyzing the completeness graph and user data, it extracts the minimal subset of questions that must be answered to reliably determine applicable scenarios, removing unnecessary questions while maintaining qualification completeness.
Solution Approach 2:
The system adapts the interview workflow to each user's specific context by dynamically determining which questions are relevant. Instead of applying the same uniform set of questions to all users, it tailors the question sequence and content to the user's particular situation, making the interaction simpler while ensuring all necessary information is collected for accurate scenario determination.
3Measurement precision
If expert intervention is used to solicit information from users in online live systems, then accurate topic determinations are achieved, but system complexity and resource requirements increase
Solution Approach 1:
The system enables self-service by automatically performing scenario determination without requiring expert intervention. The completeness graph and compliance rules are executed autonomously to evaluate user data and determine applicable scenarios. The system serves itself by making intelligent decisions about which questions to ask and how to interpret responses, eliminating the need for human experts while maintaining determination accuracy.
Data Source
AI summary
A computer-implemented system and method for predicting rule-based compliance scenarios to implement rule-based topic determinations. A server computing device generates a compliance scenario prediction model by training a machine learning model for a topic with historical user data and cohort labels created by analyzing the scenarios in a completeness graph to predict a set of scenario cohorts that constitute a set of most probable compliance scenarios. The server computing device executes the scenario prediction model to process a user profile including data features associated with the topic to predict a scenario cohort and a compliance scenario corresponding to the predicted cohort for the user. The server computing device automatically infers one or more personalized responses to at least one question of the respective decision node based on the predicted compliance scenario.