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Can someone post or send me the image from the Ring blog dated 4/9/13 called Credit Line Strategies. I want to see the graph they use to explain it, thanks.
I don't see that particular blog post. Perhaps it was removed?
Example of a Line Assignment Matrix
(Not to be confused with the real Line Assignment Matrix used for Barclaycard Ring
)
found it after some digging
by Anna, Guest Blogger from Credit Policy
I believe the community has expressed interest in learning more about how we assign credit lines at Barclaycard Ring. So, let me take a few moments to shed some light on our strategy.
When a customer applies for credit with any lender, the lender needs to make two main decisions:
1. What is the risk of giving this applicant access to credit? Will this applicant pay us back based on his/her past behavior captured by their credit history?
2. If the answer to the second question is yes, then what will be the right credit line for the cardmember? How do we assign a line that fits the needs of our cardmembers while meeting our risk hurdles?
In order to answer the second question, we analyze historical data to figure out a way to group cardmembers with similar credit profiles that also behave in a similar way when granted credit with Barclaycard. For example, we can group together cardmembers that have their credit scores between 700 and 750 points, have no recent delinquencies on any of their credit cards, mortgages, auto loans, or home equity loans, and have at least 2 credit cards issued by other banks with non-zero balances. After we complete the analysis, we develop a grid called Line Assignment Matrix that typically is a cross-tab between expected risk and credit availability. Below is an example of what that grid could look like.
When defining Line Assignment Matrix cells (e.g. why group together “No recent delinquency and credit risk score between 681 and 750” & “Low annual income & low average balance of a credit card”) we look at all the accounts that a cardmember has on his/her credit report. We try to achieve the right mix of “good/bad” behavior within each cell. And the “good/bad” behavior is heavily determinate of the credit line that will be assigned to the cell. Let me explain why.
We define “bad behavior” as:
And the “good behavior” is:
For each cell, if an assigned line is too high, we’ll get high volume of credit losses which will be hard to offset with “good” behavior within the same cell. The opposite is also true – if we assign lines that are too low, we’ll get fairly low credit losses but lots of inactive customers and again, not enough “good” behavior to generate profit. That is why it’s very important to pick the right factors (e.g. risk scores and Annual Income) to create an effective segmentation of customers into the Line Assignment Matrix.
Having said all that, we are very excited to be a part of such an innovative product, Barclaycard Ring. A lot of work and data analysis went into our initial launch strategy. As the community continues to grow and mature, we’ll be constantly reviewing and redesigning our strategies to ensure that we achieve equilibrium between community needs for credit availability and risk tolerance.
Thanks again, it's a very good read and the graph is interesting.