Scable and interactive visual analysis of financal wire transactions for fraud detection
Information Visualization - Special issue on visual analytics science and technology
Building and applying a human cognition model for visual analytics
Information Visualization
Recovering reasoning processes from user interactions
IEEE Computer Graphics and Applications - Special issue on sketching tangible interfaces augmented reality on mobile phones
Foundations and frontiers in visual analytics
Information Visualization
Science of analytical reasoning
Information Visualization
Challienges for visual analytics
Information Visualization
Exploring the inventor's paradox: applying jigsaw to software visualization
Proceedings of the 5th international symposium on Software visualization
Querying event sequences by exact match or similarity search: Design and empirical evaluation
Interacting with Computers
Investigative analysis across documents and drawings: visual analytics for archaeologists
Proceedings of the International Working Conference on Advanced Visual Interfaces
The semantics of clustering: analysis of user-generated spatializations of text documents
Proceedings of the International Working Conference on Advanced Visual Interfaces
A visual analytics tool for system logs adopting variable recommendation and feature-based filtering
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations in discovering those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships among keywords and accounts over time. Furthermore, we introduce a search-by-example technique which extracts accounts that show similar transaction patterns. In collaboration with the Anti-Money Laundering division at Bank of America, we demonstrate that using our tool, investigators are able to detect accounts and transactions that exhibit suspicious behaviors.