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Synchrony Financial
Response to the
Financial Crisis
Synchrony Financial
 110 million open accounts
 Eight decades providing customers with credit and
financing solutions
 Largest provider of private label credit cards in the US
 200,000 partners in retail, manufacturing, buying groups
and associations
 Focus on creating stronger engagement between buyers
and brands
 Tools and technologies – proprietary analytics and
tailored loyalty programs
 Improve upon current applicant risk evaluation
 Decrease default rate
 Improve consumer relationships
 Increase ROI
 Decrease capital losses
Our Goals
Current State
 Synchrony Financial default rate = 30%
What is a Random Forest?
 An ensemble of decision trees
 Accurate with supervised learning situations
 Efficient when working with large data sets
 Easy to build, tune, and adjust
 Allows us to identify most important variables
Pilot Results
 133 defaults per 575 loans or 23.13% default rate
 But is it a good fit?
But can we do better?
Pilot Modification
 Why modify?
 Decrease over-fitting
 Remove highly correlated variables
 Goals:
 Eliminate variables that could skew results
 Identify variables (applicant characteristics) to look for
 Ensure our model fits well and is useful
Modification Results
Most Important Variable?
Conclusions
 Anonymity
 Improved accuracy
 Increased ROI
 Increased relationships
Thank you.

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Andrew Lallos Graduate Capstone Presentation

  • 1. Synchrony Financial Response to the Financial Crisis
  • 2. Synchrony Financial  110 million open accounts  Eight decades providing customers with credit and financing solutions  Largest provider of private label credit cards in the US  200,000 partners in retail, manufacturing, buying groups and associations  Focus on creating stronger engagement between buyers and brands  Tools and technologies – proprietary analytics and tailored loyalty programs
  • 3.  Improve upon current applicant risk evaluation  Decrease default rate  Improve consumer relationships  Increase ROI  Decrease capital losses Our Goals
  • 4. Current State  Synchrony Financial default rate = 30%
  • 5. What is a Random Forest?  An ensemble of decision trees  Accurate with supervised learning situations  Efficient when working with large data sets  Easy to build, tune, and adjust  Allows us to identify most important variables
  • 6. Pilot Results  133 defaults per 575 loans or 23.13% default rate  But is it a good fit?
  • 7. But can we do better?
  • 8. Pilot Modification  Why modify?  Decrease over-fitting  Remove highly correlated variables  Goals:  Eliminate variables that could skew results  Identify variables (applicant characteristics) to look for  Ensure our model fits well and is useful
  • 11. Conclusions  Anonymity  Improved accuracy  Increased ROI  Increased relationships