Improving Risk ManagementBy Dennis McCafferty | Posted 2010-08-20 Email Print
WEBINAR: On-demand webcast
Next-Generation Applications Require the Power and Performance of Next-Generation Workstations REGISTER >
Technology as a business tool helps the battered financial sector make better use of data in order to reduce operating costs and increase revenue.
Of the 5 million credit card applications processed each year by Sioux Falls, S.D.-based Premier Bankcard, 2.5 million are submitted by people who fall below the FICO median credit score of 660, which deems them “subprime” customers. Since 25 percent to 35 percent of these customers will default on
the first payment, Premier is improving risk management so it can provide credit to customers who are more likely to pay.
“As we come up with pricing models, we’re factoring in the cost of risk loss,” says Rex Pruitt, who manages profitability/risk systems for Premier. “The ‘good risk’ has to be good enough to pay for the bad. Every good card we approve helps our bottom line.”
To reduce risk, Premier depended on credit scores from FICO and other providers, but the credit score alone doesn’t tell enough of the story behind each applicant to make a good business decision. So the company turned to Enterprise Miner, a data-crunching tool from SAS that allows Premier to feed thousands of variables into the database it uses to guide its risk management practices.
“We can feed the FICO score, but we can do so much more than that,” Pruitt says. “For example, we can input whether an applicant has had a collection notice due for more than $100 within the last six months, or whether the applicant has ever filed for bankruptcy, and, if so, how long ago. We can take into the account the frequency rate of payment delinquencies. A FICO score may be low because of something that happened a long time ago, as opposed to within the last six months.”
Even those who fall short of a good credit score will often get a card, but the data mining solution allows Premier to identify those who will need greater monitoring for fraud-control mitigation. The reduction of fraud cases has resulted in $9 million in annual savings.
In addition, Premier is saving $21.3 million a year in the avoidance of what it calls “soft fraud” cases: those in which customers secure a card without any intention of ever paying off any charges. Finally, Premier can now process and analyze information 75 percent faster than before.
Speed is also critical in the trading industry, where those who produce the quickest, most accurate numbers end up on the winning side. So technology leaders in this field are always looking for an added edge when it comes to
Take the New York-based Inter-national Securities Exchange. As one
of eight options exchanges in the country, ISE provides two-sided quotes for more than 275,000 options series that trade on the exchange, and that can generate as many as a billion quotes on a peak-performance day—the equivalent of more than 25 gigabytes of data.
ISE sends this data to the Options Price Reporting Authority (OPRA), which aggregates quote and trade information for the entire industry. “The U.S. options market is driven by a huge amount of data,” says Jeff Soule, head of market data for ISE.
“Intra-day peak message rates can reach more than 1 million messages per second. In this case, a ‘message’ is a quote or a trade. When the market is moving quickly, the data is moving quickly right along with it. Speed and capacity are both critical to compete in this industry.”
In 2009, ISE decided to enhance its front-end trading application, PrecISE Trade, with the introduction of options analytics. It ultimately turned to Hanweck Associates to launch its VoleraFEED options analytics solution, which can perform tens of millions of options valuations per second. Price feeds, dividend maps, yield curves and rebate curves are all part of the data that the analytics engine can crunch.
Most significantly, VoleraFEED provides ISE’s members with the tools to determine an option’s volatility, or the anticipated percentage of movement (up or down) on the option’s price over a given period of time. That’s a key determining factor in pricing the option.
Whether financial institutions want to predict customer behavior, manage mergers and acquisitions, reduce exposure to risk or perform millions of transactions per second, technology tools can enable and support those objectives.