Customer Risk Visualization Aggregating Multi-Source Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional tools for evaluating customer risk lack a holistic approach, failing to aggregate and visualize data effectively, especially for customers with complex transaction patterns across multiple accounts and countries, and those involved in electronic transfers.
Innovation Solution
A computer-implemented system that models customer risk profiles by aggregating data from internal and public sources, including payment screening, suspicious activity reports, and financial crimes data, to provide a customer-centric risk visualization tool that facilitates real-time due diligence and transaction monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tools evaluate risks from individual sources separately, then data from each source can be analyzed in detail, but a holistic risk evaluation cannot be achieved
Solution Approach 1:
The patent merges data from multiple individual risk sources (payment screening, SAR lists, monitored transactions, onboarding assessments) into a unified customer risk profile. The system aggregates these separate data streams and presents them through a single holistic view, allowing comprehensive risk evaluation while managing complexity through integrated data structures and unified presentation layers.
2Loss of information
If conventional tools gather data from distinct data sources, then diverse information can be collected, but meaningful aggregation and visualization are not provided
Solution Approach 1:
The system introduces an intermediary layer that collects raw data from multiple distinct sources, processes and structures it into meaningful risk attributes, and then presents it through visualization tools. This intermediary processing layer transforms disparate data into organized risk profiles that are both information-complete and easily visualizable through graphs, charts, and structured displays.
3Adaptability or versatility
If conventional tools fail to accommodate customers with complex transaction patterns, then system simplicity is maintained, but comprehensive risk assessment for complex cases is lost
Solution Approach 1:
The system dynamically adapts to accommodate complex transaction patterns by implementing flexible data structures and processing logic that can handle variable complexity. The risk visualization tools dynamically adjust to display appropriate levels of detail based on the customer's transaction complexity, allowing the system to maintain simplicity for basic cases while providing comprehensive analysis for complex scenarios.
Data Source
AI summary
A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.