NLP AI Search System for Entity Relationship Analysis
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Solution Overview
Problem
Conventional Internet searches are time-consuming and often fail to yield accurate results, particularly in identifying relationships between entities relevant to financial crimes or negative news, which is crucial for Know Your Customer (KYC) processes and other regulatory compliance tasks.
Innovation Solution
A natural language processing (NLP) and artificial intelligence-based search system that utilizes machine learning techniques to conduct customized entity-driven searches, analyzing relationships between entities and providing bi-directional graphs to identify nodes associated with negative news or predetermined criteria, ranking search results based on specified criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines are used to search for entities and relationships, then basic search functionality is provided, but the search process is time-consuming and accuracy is low
Solution Approach 1:
The system pre-processes and structures relationship data from multiple sources into a standardized format with defined entity types and relationship categories before searches are executed. This preliminary organization of data into structured relationship profiles enables rapid retrieval and significantly improves both search speed and accuracy without requiring complex real-time processing
Solution Approach 2:
The system creates structured copies of relationship data from various unstructured sources (news articles, social media, databases) and stores them in a standardized relationship schema. These pre-processed copies can be quickly queried and analyzed, eliminating the need to re-process original sources during search operations
2Loss of information
If manual analysis of search results is performed to identify relationships between entities, then comprehensive relationship identification is possible, but the process is extremely time-consuming
Solution Approach 1:
The system replaces manual mechanical analysis with automated natural language processing and machine learning algorithms that can identify and classify relationships between entities at scale. The NLP models automatically extract relationship types (e.g., ownership, employment, familial) from text and structure them according to predefined schemas, achieving both completeness and speed
Solution Approach 2:
The system enables automated self-service relationship extraction where the NLP models independently identify, classify, and structure relationships without human intervention. The automated classification system continuously learns from feedback and improves its relationship identification capabilities while maintaining high-speed processing
3Measurement precision
If traditional search methods are used to identify entities related to financial crimes, then basic search capability is provided, but accuracy in identifying relevant entities is low
Solution Approach 1:
The system applies specialized relationship classification rules and NLP models tailored to specific domains such as financial crimes, regulatory compliance, and sales prospecting. Each domain has customized entity types, relationship categories, and classification criteria that enhance accuracy for that specific application without requiring complete system redesign
Solution Approach 2:
The system introduces structured relationship profiles as an intermediary layer between raw data sources and search queries. These profiles organize entity relationships into standardized formats with defined attributes and categories, making complex relationship data accessible through simple search operations while maintaining high accuracy
Data Source
AI summary
In some examples, natural language processing (NLP) and artificial intelligence based searching may include identifying named entities in text from a corpus of documents. References in the text may be resolved with the identified named entities. Links between the named entities may be determined, and a bi-direction rootless graph may be generated. Semantic relationships may be determined from text of the named entities, and blacklist keywords may be identified. Machine learning classification may be performed based on a pair of the named entities and a blacklist keyword. A classification may be determined based on the pair of named entities and the blacklist keyword, and a rule may be identified that specifies which named entity in the pair is to be flagged. Further, a node in the graph may be flagged based on an association with the named entity identified according to the rule.