Graph Database for Financial Crime Risk Visualization
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Solution Overview
Problem
Current systems for monitoring and evaluating financial crime and sanctions-related risks in banking and financial systems are inadequate in providing a quick and cogent representation of risks, as they struggle with processing and interacting with vast amounts of information effectively.
Innovation Solution
A computer-implemented system utilizing a graph database and style guide to store and analyze nodes, edges, and properties, which includes an internal production environment for analysts to input and traverse pathways, and an external production environment for customers to visualize and interact with risk corridors, enabling efficient identification and visualization of financial crime and sanctions-related risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional systems are used to monitor and evaluate financial crime risks, then they can process information through conventional databases, but they fail to provide a quick and cogent representation of risks due to the vast amounts of information
Solution Approach 1:
The patent transitions from traditional tabular databases to a graph database structure, adding a dimensional aspect of interconnected relationships between entities. This allows the system to represent complex financial crime risks by visualizing connections between sanctioned and non-sanctioned entities through nodes and edges, enabling faster and more intuitive risk assessment while handling vast amounts of information.
2Reliability
If comprehensive data storage is implemented to capture all financial transaction information, then complete risk coverage is achieved, but the complexity of the system increases making it difficult to interact with and analyze the data
Solution Approach 1:
The patent segments the comprehensive financial data into discrete nodes (representing entities) and edges (representing relationships) within a graph database structure. This segmentation allows analysts to interact with specific portions of the data relevant to their investigations without being overwhelmed by the entire dataset, while still maintaining complete risk coverage through the interconnected graph structure.
Solution Approach 2:
The graph database acts as an intermediary layer between the comprehensive data storage and the user interface. It provides a structured framework that simplifies complex relationships into visualizable nodes and edges, making it easier for analysts to interact with and analyze the data while preserving the complete information necessary for thorough risk assessment.
3Measurement precision
If detailed analysis tools are provided to analysts for investigating financial crime risks, then the depth of risk assessment improves, but the time required to investigate and evaluate risks increases
Solution Approach 1:
The system pre-structures data into a graph database with predefined nodes, edges, and properties before analysis begins. This preliminary organization of data into meaningful relationships allows analysts to immediately begin their investigations with pre-sorted and connected information, reducing the time required to assess risks while maintaining detailed analysis capabilities through the rich graph structure.
Data Source
AI summary
A research, analysis, regulatory compliance and media platform that connects customers to finished research and analysis produced by subject matter experts is described. The platform facilitates research, investigations, and analysis by creating a single environment in which a group of distributed analysts conduct research and investigations, store and retrieve documents and other sources, collaborate, and publish findings. Consumers are able to query a published knowledge graph, surface high value relationships, and access insights captured by analyst through a customer web portal or external production environment. The platform allows analysts and customers to research and map the commercial, financial, and facilitation networks of sanctioned or other actors that may be associated with illicit activity. Customers can access visual graphs depicting relationships between sanctioned and non-sanctioned actors in order to evaluate their possible exposure to financial crime or sanctions-related risks.