- Completely streamlining a process to deliver measurable, timely, credible and meaningful insights closer to the customer’s decision is the vision.
- Managing the risk/reward equation to achieve sustainable competitive advantage, predictability and higher profitability is the goal.
Earlier in my career, I worked as a Pricing Manager in the auto finance industry. During my first weeks, the common topic of “Adverse Selection” and its negative impact on profitability, pricing response models and credit risk models was discussed in several meetings. This was a new term for me so I walked into my manager’s office and asked him to explain adverse selection to me. His answer:
“I’m not exactly sure how to explain it, but I know it when I see it”
He went on to describe the impact of pricing on the customer’s decision to buy. The biggest fear is that higher risk credits take advantage of poor pricing models and thus, blow up the ability to forecast losses properly. My passion for bringing complex analysis to the entire organization and deliver on the vision and the goal was born.
Bringing the Data Scientist Work to the Point of Decision:
For starters, building applications from data/output of traditional analytics software (SAS, SPSS, Matlab and R) is an extremely common use case in Financial Services. However, the analytics market often gets distracted/confused by the differences between "integration" and “connectivity". My response is yes, Qlik customers do both, plus a whole lot more.