Discriminative Predictor for Risk Control Feature Identification
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Solution Overview
Problem
Existing systems for identifying key features in risk control documents rely on rule-based methods, which are tedious, impractical, and fail to generalize well to new language, leading to inefficiencies and inaccuracies in risk control feature identification.
Innovation Solution
A hybrid machine learning and natural language processing system that analyzes risk mitigation text to automatically identify risk control features and entities, using generative ML models to transform and classify text, and discriminative predictors to correct and segment features based on syntactic characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rule-based methods are used to identify risk control features, then the system can operate with simple logic, but the identification accuracy and generalization capability deteriorate
Solution Approach 1:
The patent replaces rule-based mechanical processing with machine learning models (transformer architecture, BERT, RoBERTa) that automatically learn patterns from data. The ML system substitutes manual rule creation and execution with automated neural network processing, achieving both accuracy and adaptability without proportional increases in system complexity.
Solution Approach 2:
The patent transforms the approach from fixed rule parameters to dynamic learned parameters. The system uses trainable embeddings, attention weights, and model parameters that adapt to different contexts and languages, enabling high accuracy across diverse risk control documents without requiring manual rule updates for each scenario.
2Device complexity
If rule-based methods are used to identify risk control features, then the system structure remains simple, but the ability to generalize to new language deteriorates
Solution Approach 1:
The patent implements a universal ML-based system that handles multiple languages, document types, and risk control scenarios through a single transformer architecture. The model is trained on diverse datasets and can generalize to new languages and contexts without requiring separate rule sets, achieving multi-functionality while maintaining manageable system structure through standardized processing pipelines.
Solution Approach 2:
The patent performs preliminary training of ML models on large corpora of risk control documents before deployment. This pre-training phase allows the system to learn linguistic patterns, domain terminology, and feature relationships in advance, enabling rapid adaptation to new languages and document types without extensive reconfiguration or rule rewriting.
3Ease of operation
If manual rule creation is used, then the system can be easily understood, but the time and effort required increases significantly
Solution Approach 1:
The patent implements self-service through automated ML models that independently identify and classify risk control features without human intervention. The system automatically processes documents, extracts features, and generates results, eliminating the time-consuming manual rule application process while maintaining transparency through explainable AI techniques that preserve understandability.
Solution Approach 2:
The patent incorporates feedback mechanisms where model predictions are evaluated and used to refine future processing. The system learns from correction feedback and continuously improves its feature identification accuracy, reducing the time required for manual verification and rule adjustment while maintaining system transparency through interpretable decision pathways.
Data Source
AI summary
Embodiments disclosed are directed to a computing system that performs steps to automatically identify risk control features and entities in a risk control document. The computing system uses a generative machine learning (ML) model to transform a risk control document into sequences of words, classify risk control features associated with the sequences of words, and pair the sequences of words with the classified risk control features. The computing system then uses a natural language processing (NLP) model to identify syntactic characteristics of the sequences of words. Subsequently, the computing system uses a discriminative predictor system to correct the classified risk control features based on the identified syntactic characteristics, identify boundaries of the corrected classified risk control features, and pair the identified boundaries with the corrected classified risk control features.