Data scientists at Capital One and BuildingIQ use data visualizations to help guide their efforts in developing, training and modifying predictive models for advanced analytics.
Recently, Capital One data scientist Brendan Herger worked on a predictive analytics project aimed at identifying potentially problematic bill totals when diners using a Capital One credit card add a tip to their tabs after the initial swipe of their card.
The goal, he said, is to help cardholders avoid accidentally leaving inappropriately large tips. The analytics application triggers a text message or email to a Capital One customer if it spots a likely discrepancy, for example, a math error that the restaurant didn’t catch. The cardholder can then dispute the charge with either the restaurant or Capital One itself.
To develop the required analytical functionality, Herger and his colleagues at the banking and service company, based in McLean, Va., had to “train” thepredictive model on historical data to define what inappropriate tips look like so they can be flagged during incoming transactions. On this type of analytics project, visualized data helps the data scientists monitor the model training process and make sure the conclusions generated by a model fit real-world scenarios. Herger said that if a data visualization shows a big spike in the analytics data at a given point compared to what came before or after, he knows something may be off in the model’s algorithms, requiring additional development work to set it right.
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