jilldeckert@fico.com

Evolving from Judgment to Data Driven Strategies

Blog Post created by jilldeckert@fico.com Advocate on May 15, 2018

This blog series features the opinions and experiences of five experts working in various roles in global strategy design. They were invited to discuss the best practices outlined in this white paper, and also to add their own. With combined experience of over 70 years, they partner with various businesses and help them to use data, tools and analytic techniques to ensure effective decision strategies to drive success. The experts share their personal triumphs and difficulties; you’ll be surprised to learn the stark differences, and occasional similarities, in these assorted expert approaches to accomplishing successful data driven strategies across industries.

 

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Jill Deckert is a Principal Consultant at FICO where she’s worked for the past 11 years. Jill works collaboratively with her clients to provide an actionable roadmap of improvements. Throughout the process, she considers her clients’ current business constraints and understands the wider context of their business.

 

From Judgment to Data Based Strategies

Judgment-based strategies are effective at carrying out decision processes based upon established guidelines. These strategies are usually developed according to a stack of business rules, often referred to as knock-out rules. Strictly following these steadfast rules can often lead to unintentionally excluding profitable accounts, which leaves the business with a smaller target population and reduces their opportunity for growth.

 

Data-driven strategies are built using historical data. With a data driven approach, the construction of the strategy is guided by how the data reacts to different decision elements and threshold values. Therefore, the best treatment can be assigned for different sub-populations of accounts. These types of strategies are often a balance between judgmental criteria (i.e. business rules) and data science. Data driven strategies can be much more effective than a judgment based strategy; however, the transition from judgment based to data driven requires not only an operational shift, but an institutional shift as well.

 

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Upfront preparation, buy-in, proof and trust are key components to success, as discussed by my colleagues earlier in this series. Since the data extraction process can be difficult and time consuming, it is common to see lenders relying on established processes. However, embracing a different approach to building strategies could result in improved success and greater profit potential.

 

Case Study
My team and I worked with an automobile lender to build an origination strategy. They had been lending for more than 20 years, and were using established knock-out rules to identify risky populations. Their goal for the new strategy was to maintain their existing bad rates while increasing automation rates (i.e. reduce the number of manual reviews).  

 

We began planning the transition from a judgmental to a data driven strategy by mapping out their current champion strategy and comparing it to the new challenger strategy that was built with data driven decisioning (learn more about Champion/Challenger testing here). We spent half a day walking through the strategy and reviewing their business rules to show how the existing rules were eliminating profitable segments of their population, but not reducing any incremental risk.

 

We were also able to show that a large percent of their manual review population could be automatically approved based on additional decisioning criteria they weren’t already using. During the course of this project we were able to demonstrate how a phased approach to implementing a data driven strategy can more effectively eliminate risk and allow for increased auto-approval rates.

 

We agreed on several critical business rules to be included and then incorporated them into a “hybrid” data driven strategy.

 

The success of this project is due in large part to the use of FICO’s Decision Tree Professional. This tool helps identify the most predictive variables to use. We were able to show how different decision keys and key splits impacted the decisions and outcomes. This exercise was extremely useful in demonstrating how certain decision keys are more effective at eliminating bads from the target population.

 

At the end of the day, the lender was much more comfortable with the data driven approach. The ability to visually show the data within the strategy tool and the comparison of results between the champion and the challenger strategy provided compelling reasons to move on from a judgmental approach to a data driven approach. It was clear that the challenger strategy was more efficient and effective.

 

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Bad rates by strategy

 

After shifting from a risk focused, judgmental approach to a data driven strategy approach, the next step is to implement mathematical optimization. You can look forward to hearing about this step from my colleague Sonja Clark later on in this series.

Are you relying on knock out rules to dictate your strategy decisions? Discuss the benefits of introducing data driven decisioning into your strategy below in the comments, or in our TRIAD and Analytic Tools communities.

If you’re a current TRIAD user, join us at our upcoming Customer Forum, May 23-24 in Atlanta, Georgia.

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