1. BIN Level Analytics – Payments are not Logical but Data Don’t Lie
Bill Cohn – Vantiv
Jay Matthews – AOL
Fan Yang -- Microsoft

2. Overview Problems To Be Solved The 3 Phases of Analysis
Inefficiencies of the Payments Ecosystem
The 3 Phases of Analysis
Inform
Infer
Influence
Best Practices – Practical Experience
Guest Panelists

3. The Problem with Payments
Participants in the payments ecosystem are not well aligned
Overly aggressive fraud filtering
False positive decline
Customer
Merchant/
Gateway
Merchant Processor / Acquirer
Network
Issuer Processor
Issuer
Mis-keyed account number
Recurring transaction not recognized
New merchant network ID damages continuity

4. Inform

5. Inform: Shine a Light on Issuer “Behavior”
The first step in solving a problem is recognizing that it exists.
-- Zig Ziglar

6. Inform: Sources of Data
Where do you get accurate, comprehensive (global) BIN/Issuer data?
Third parties?
Your payments processor?
How deep is the data you get?
Does it go to the BIN level? How many BINs does it provide?
Does it go to the Account Range, i.e. card product level?
Can you discern issuing bank decline rates by primary reason codes?
How frequently is it updated?
Bi-annually? Monthly? Weekly?
Does the frequency match your business needs/use cases?
Does it reflect the rate of change in the data?

7. Inform: Data Elements
What data do you receive? What data do you derive?
Card characteristics?
Consumer/Corporate
Funding Source: Credit; Debit (Regulated/Not-Regulated; Prepaid (Reloadable/Non- Reloadable); FSA/HSA
Issuer Country of Origin
Affluence level of cardholder (based on card product qualification)
Approval rates for each issuer/card product?
Do you derive this yourself?
How do you benchmark your approval rates?

8. Public Sources

9. Advanced Data Sources

10. Infer

11. Infer: What Insights Can Analytics Provide?
Spikes in decline rates for specific issuers – Red Flag!
Card products you may not want to accept for recurring billing
Card products and issuers that yield highest approval rates
Cardholder demographics
Influx of new cards in market
Issuers not participating in Account Updater programs
Portfolio swaps
Risk scoring – which banks are more likely to issue chargebacks?

12. Influence

13. Influence: What Actions Can you Take?
Reach out to issuers directly – or via processor – for explanations of decline spikes
Customize retry strategies by card type, by issuer
Reach out to cardholder immediately after a decline if the issuer doesn’t participate in account updater
Tender steering – provide incentive to consumers to use higher yield card products
And …

14. Practical Experience

15. Jay Matthews
Director, Global Payments and Risk
AOL
Chairman – PaymentsEd Forum
Fan Yang
Business Analyst
Microsoft