WireVis: Visualization of Categorical, Time-Varying Financial Transaction Data

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.

Remco Chang, Mohammad Ghoniem, Robert Kosara, William Ribarsky, Jing Yang, Evan Suma, Caroline Ziemkiewicz, Daniel Kern, and Agus Sudjianto, WireVis: Visualization of Categorical, Time-Varying Financial Transaction Data, IEEE Symposium on Visual Analytics Science and Technology (VAST), pp. 155–162, 2007. DOI: 10.1109/VAST.2007.4389009

@inproceedings{Chang:VAST:2007,
	year = 2007,
	title = {WireVis: Visualization of Categorical, Time-Varying Financial Transaction Data},
	author = {Remco Chang and Mohammad Ghoniem and Robert Kosara and William Ribarsky and Jing Yang and Evan Suma and Caroline Ziemkiewicz and Daniel Kern and Agus Sudjianto},
	booktitle = {IEEE Symposium on Visual Analytics Science and Technology (VAST)},
	pages = {155–162},
	doi = {10.1109/VAST.2007.4389009},
	abstract = {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.},
}