1. Credit Scoring Models ICTF – Ft Lauderdale, FL November 13, 2012
Scott Tillesen
Vice President, US Credit Services
Tech Data Corporation

2. ICTF November 13, 2012 Agenda Today’s three topics:
Background on Tech Data credit organization.
Tech Data history of scoring models.
Recent transition to integrated scoring tool.

3. About Tech Data Corporation
Global distributor of computer hardware, software, and consumer electronics.
$27+ Billion annual revenue.
Product brands include: HP, Cisco, Apple, IBM, Lenovo, Symantec, Microsoft, NEC, Sony, and 100’s of others.

4. Tech Data Credit Operations - Europe
16+ credit offices
Transitioning from insured risk:
Trade references
People
Policies and practices
Systems (including scoring)

5. Tech Data Credit Operations – The Americas
9 credit offices
Almost exclusively owned risk.
Centralized US credit office.
Scoring (of various kinds) in use for fifteen years.

6. Scoring Transformation
Uplift Analysis
Off-Line Scheduled Reviews
New Account Matrix
Uplift Analysis with Sophistication
Integrated and Automated Scheduled Reviews

7. 1. Uplift Analysis Objective:
Pro-actively increase of credit limits on worthy accounts.
Elements Included:
Current credit limit
Average paydays
Account status code

8. Uplift Analysis - Results
Mass uplifts every six months (or so).
Increase the credit limits of thousands of customers.
Increased credit limits by millions of dollars.
Virtually no associable bad debt or slow pay.

9. 2. Off-line Scheduled Reviews
Objective:
Improve staff productivity.
Elements Included:
Basic “Uplift” elements – credit limit, status codes, and paydays.
Added –
Net worth
External scores
Previous payment plan
Security

10. 2. Off-line Scheduled Reviews – Results
Drove next review date.
Notes added to legacy system via macro.
Handled over 50% of reviews with credit limits under $200K.
Virtually no associable bad debt or slow pay.

11. 3. New Account Matrix Elements Included: Personal Guaranty
Objective:
Improve consistency of decision and credit staff productivity during the review of new accounts.
Elements Included:
Personal Guaranty
Signer’s personal credit score

12. 3. New Account Matrix – Results
Consistent decisions.
Improved decisions.
Faster processing.
Avoided need for financial statements.
Credit limit range: $5K to $20K
Virtually no associable bad debt or slow pay.

13. 4. Uplifts with Sophistication
Objective:
Pro-actively increase credit limits on worthy accounts.
Elements Included:
Basic “Uplift” elements – credit limit, status code, and paydays.
Added – % past due, account age, net worth, external scores, previous payment plan, security, and credit utilization.

14. 4. Uplifts with Sophistication – Results
Added variables improved confidence.
Recent uplift of 1,700 accounts by $25 million.
Identification of 120 additional accounts for likely increase.

15. 5. Integrated and Automated Scheduled Reviews
Objective:
Further staff productivity improvement and added sophistication.
Elements Included:
Traditional (38%): Age of account, F/S date, credit app signer credit score
Payment portion (49%): NACM risk class score, Experian Intelliscore, Smyyth PQI score, previous payment plan, and paydays.
Z score (13%)
Kick out factors: stale f/s, bad external scores, net worth, status code

16. 5. Integrated and Automated Scheduled Reviews - Results
Required custom risk management system development.
Handles over 50% of reviews with credit limits under $300K.
Virtually no associable bad debt or slow pay.

17. Future Possibilities Socialize the scoring model score.
Socialize the model’s recommended credit limits.
Statistical identification of significant variables.

18. Scott Tillesen Vice President, U. S
Scott Tillesen Vice President, U.S. Credit Services Tech Data Corporation