Extractive Document Summarization for Risk Factor Detection

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Solution Overview

Problem

Conventional AI-based risk management tools are limited in effectively retrieving, analyzing, and presenting risk factor information for money laundering detection, leading to inefficiencies in investigations and risk mitigation processes.

Innovation Solution

The implementation of an extractive document summarization model combined with machine learning predictive modeling for topic-based summarization, utilizing a graphical user interface to display risk factor visualizations and summaries, enhances the detection and identification of risk factors related to money laundering and criminal activities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional AI-based risk management tools are used, then basic risk factor identification is possible, but information retrieval efficiency and analysis accuracy are limited

Engineering Contradiction:

Improveinvestigation productivityVSAvoidrisk factor information completeness

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system extracts and summarizes key risk factor information from large volumes of documents using extractive document summarization models. This extraction process isolates the most relevant information about risk factors, enabling investigators to quickly access critical details without wading through entire documents, thus improving productivity while maintaining information completeness.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system segments documents into meaningful sections and identifies specific risk factor topics within them. By dividing the information into structured segments with clear topic classifications, the system enables efficient retrieval and analysis of specific risk factors while preserving the complete context and relationships between different information elements.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If comprehensive document analysis is performed, then complete risk factor information is obtained, but time consumption and processing duration increase

Engineering Contradiction:

Improverisk factor detection accuracyVSAvoidinvestigation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary document summarization and risk factor identification before detailed investigation. By pre-processing documents to extract and highlight key risk factors and their contexts, the system reduces the time required for subsequent analysis while maintaining comprehensive and accurate risk factor detection through the preserved contextual information.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual comprehensive document review with automated machine learning models for risk factor detection. These models rapidly analyze documents with high precision, identifying risk factors and their contexts efficiently without the time consumption of manual analysis, while maintaining or improving detection accuracy through advanced NLP techniques.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If detailed risk factor information is presented, then analysis completeness is improved, but information presentation complexity increases

Engineering Contradiction:

Improverisk assessment reliabilityVSAvoidinterface complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system presents risk factor information with varying levels of detail based on local requirements. The graphical user interface displays summarized risk factor information prominently, with options to access more detailed contextual information only when needed. This local quality approach ensures reliable risk assessment through complete information availability while keeping the default presentation simple and manageable.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system adds a dimensional layer to information presentation by organizing risk factors into structured categories and topics. Instead of presenting all information in a single complex view, the system uses hierarchical organization and topic-based grouping to multidimensionally structure the information, making it easier to navigate and understand while preserving complete risk factor details for thorough analysis.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS20240412143A1Systems and methods for risk factor predictive modeling with document summarization

Publication Date: 2024.12.12 BANK OF MONTREAL

AI summary

A system and method for document summarization generates summarized articles and risk factor categorizations for display at a graphical user interface (GUI) dashboard. A transaction monitoring system includes an adverse media dashboard pipeline for processing risk factor alerts and generating document summarizations for display at a user device. Document summarization extracts several sentences from a source text and stacks the sentences to create a summary. The method creates a vector representation of each sentence using a machine learning word embedding model and generates a sentence similarity matrix by computing cosine similarity values. A sentence graph creation algorithm creates a graph corresponding to the sentence similarity matrix and calculates importance scores used in selecting sentences for the document summary. The GUI dashboard includes first, second, third and fourth dashboard regions for displaying alert report records, media records, and graphical user interface layouts of document summaries and risk factor visualizations.