“When a customer applies for a loan, our answer is based on those models, which use a variety of data, including data related to the customer as well as external data, such as data from credit...

“When a customer applies for a loan, our answer is based on those models, which use a variety of data, including data related to the customer as well as external data, such as data from credit bureaus. We use Statistica to find patterns in that data related to good and bad financial behavior, predict whether a customer will default within a year, and determine credit worthiness.” • Ensuring regulatory compliance “Our models have been under increasing scrutiny [from regulators]—for example, we have had to recalibrate all our models,” Jensen explains. “In this area, we have definitely seen the benefits of the Statistica platform [as] it is so easy to use.” • Advanced modeling “With Statistica, we can do more advanced modeling than we could before,” says Jensen. “It is a key part of our larger strategy around analytics, which involves not only structured data but Big Data in our data hub.” • The scalability to meet future needs “We will need to build more and more models to use in our business processes,” notes Jensen. “We already have initiatives to put more processes on mobile devices, and we will need to gain additional insight around customer behavior to make even more automatic decisions. I’m confident that the Statistica platform can scale to meet our needs into the future.”

 Questions for Discussion

1. What were the change, proposed solution, and results of the adoption of Dell Statistica?

2. In your opinion, where else can a banking institution productively use advanced analytics?