Analytics
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Predictive analytics
The rise and value of predictive analytics in enterprise decision making
“Give me a long enough lever and a place to stand, and I can move the Earth.” Archimedes, 250 B.C.
In the past few years, predictive analytics has gone from an exotic technique practiced in just a few niches, to a competitive weapon with a rapidly expanding range of uses. The increasing adoption of predictive analytics is fueled by converging trends: the Big Data phenomenon, ever-improving tools for data analysis, and a steady stream of demonstrated successes in new applications. The modern analyst would say, “Give me enough data, and I can predict anything.” The way predictive models produce value is simple in concept; they make it possible to make more right decisions, more quickly, and with less expense. They can provide support for human decisions, making them more efficient and effective, or in some cases, they can be used to automate an entire decision-making process. A classic example of predictive analytics at work is credit scoring. Credit risk models, which use information from each loan application to predict the risk of taking a loss, have been built and refined over the years to the point where they now play indispensable roles in credit decisions. The consumer credit industry as we know it today could not operate without predictive credit risk models. Credit scoring is demonstrably better than unaided human judgment in both accuracy and efficiency when applied to high volume lending situations such as credit cards. So much so, that any company in the credit industry that does not use it is at a significant competitive disadvantage.
1
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© 2013 CGI GROUP INC.
About this paper
Predictive analytics is on the rise as the number of successful applications continues to increase. Predictive models can be used to generate better decisions, greater consistency, and lower costs. Top areas in which predictive models are generating
Predictive analytics
The rise and value of predictive analytics in enterprise decision making
“Give me a long enough lever and a place to stand, and I can move the Earth.” Archimedes, 250 B.C.
In the past few years, predictive analytics has gone from an exotic technique practiced in just a few niches, to a competitive weapon with a rapidly expanding range of uses. The increasing adoption of predictive analytics is fueled by converging trends: the Big Data phenomenon, ever-improving tools for data analysis, and a steady stream of demonstrated successes in new applications. The modern analyst would say, “Give me enough data, and I can predict anything.” The way predictive models produce value is simple in concept; they make it possible to make more right decisions, more quickly, and with less expense. They can provide support for human decisions, making them more efficient and effective, or in some cases, they can be used to automate an entire decision-making process. A classic example of predictive analytics at work is credit scoring. Credit risk models, which use information from each loan application to predict the risk of taking a loss, have been built and refined over the years to the point where they now play indispensable roles in credit decisions. The consumer credit industry as we know it today could not operate without predictive credit risk models. Credit scoring is demonstrably better than unaided human judgment in both accuracy and efficiency when applied to high volume lending situations such as credit cards. So much so, that any company in the credit industry that does not use it is at a significant competitive disadvantage.
1
cgi.com
© 2013 CGI GROUP INC.
About this paper
Predictive analytics is on the rise as the number of successful applications continues to increase. Predictive models can be used to generate better decisions, greater consistency, and lower costs. Top areas in which predictive models are generating