In the wake of the attacks on the World Trade Center and Pentagon, questions persist about whether routine efforts to recognize and communicate potential warning signs could have staved off disaster. In the aftermath of the tragedy, airlines and others entrusted with people's lives are likely to adopt some of the risk management techniques employed by financial institutions, experts say.
"Things that could have been done weren't," says Jim Eckenrode, head of consumer banking research at TowerGroup (Needham, MA). "Investments in technologies that banks have made related to fraud detection in real-time may be useful in other industries."
Prodded in part by Congress, many banks have installed money laundering detection systems from vendors such as Americas Software (Miami), Atchley Systems (Dallas), Prime Associates (Clark, NJ) and SearchSpace (London).
As investigators pored through banking records and credit card transactions to trace the hijackers' steps, the US Treasury announced the establishment of an interagency team dedicated to the disruption of terrorist fundraising. The team, which is to be transformed into a permanent Foreign Terrorist Asset Tracking Center within the Office of Foreign Asset Control (OFAC), is designed to identify foreign terrorist groups and their sources and methods of fundraising.
Financial institutions use a wide range of fraud-detection techniques, employing both human judgment and sophisticated data modeling. Some systems distill expert knowledge into a set of rules that can be applied to each transaction ("rules-based systems"). San Diego-based HNC Software's Falcon uses neural-network techniques to build models of complex transaction patterns such as consumer credit card fraud. The models are "trained" to recognize patterns through an iterative process in which large numbers of transactions are passed through a neural network algorithm. Once training is complete, the neural network uses these learned patterns to predict the probability that a new individual will exhibit the modeled transaction patterns.
In addition to detecting fraud, predictive software solutions play a key role in other business functions. For example, within a customer relationship management (CRM) system, predictive software analyzes customer data and other information to predict what the customer will do today and in the future. ERP systems provide transaction-level data that can be analyzed by predictive software solutions to optimize inventory management and enhance supply chain logistics. When predictive software is embedded in a real-time transaction stream, it can yield intelligence enabling companies to adopt business strategies to ensure maximum customer profitability.
Data is the key ingredient in risk management systems, which by definition attempt to predict the likelihood of unforeseen events, such as a sharp drop in securities prices (market risk), a loan default (credit risk), or natural and man-made disasters (operational risk).
Market and credit risk systems are blessed with rich sources of data, and hence are fairly robust. But operational risk is harder to quantify. Cases of rogue employees taking down firms, such as Nick Leeson at Barings Bank, or of major systems being compromised are rare and seldom publicized. "It's hard to define a statistically significant populace of fraud or the sorts of debacles that could bring down firms," says Peter Keppler, a research analyst at Meridien Research (Newton, MA).