Update – link to Reddit post/discussion on Bust Out Fraud.
Please note the “may” in the title. I don’t know for sure whether it does or not, but I present a case for why it could, which I think is worth considering for yourself.
TravelinPoints reports a disturbing data point of an irreversible Chase shutdown due to churning, i.e. a high number of recent inquiries/accounts. I did some research and found this Experian white paper on Bust-out fraud (HT: Flyertalk member Mamibear). You should read it, but here’s a summary:
- Definition: bust-out is when a consumer builds up a good credit profile, only to suddenly max out credit lines and vanish, leaving a large amount of debt unpaid.
- This kind of fraud is on the rise, and banks want to curtail it.
- The predictors of bust-out are very similar to the profile of a churner.
The last bullet point is key. Experian developed a BustOut Score to identify potential perpetrators. Consider this plot from the white paper:
As you can see, of the 4 characteristics illustrated, 2 (# of inquiries, # of new cards) occur before the onset of bust-out, and 2 (credit utilization, delinquencies) occur after the onset. It’s highly likely that the former are used as predictors of bust-out, in some fashion. The paper doesn’t detail the exact components of the BustOut Score, but does list some characteristics, including:
- Age of oldest trade: perpetrators score well, but short of average consumers.
- Age of most recently opened trade.
- # of trades opened in last 12 months.
- % of credit cards to total open trades.
- # of inquries
- # of real property trades.
Many of these characteristics are driven by # of inquiries and new accounts. To be clear, we’re talking about #s across all banks, albeit on a single credit report (which can change as algorithms get better). Bust-out perpetrators have a large number, as do churners. Just as MS’ers are sometimes mistaken for money launderers, churners can be mistaken for bust-out fraud. Specifically, I want to highlight one characteristic from the graphic: # of inquiries from last 12 months and how this may be influenced by App-O-Rama (AOR). Consider an example year of churning:
Now consider the metric (that is, # of inquiries in 12 months) in month #12. Going back 12 months, there are a total of 9 inquiries (2+4+3). However, in month #13, the metric increases to 14 inquiries (2+4+3+5). IF a bank uses the change in # of inquiries over 12 months as a predictor of bust-out fraud (and from 9 to 14 can be interpreted as a big change, depending on the algorithm), OR just # of inquiries straight up, you can see how an AOR could potentially put you on the radar. Of course, the bank is very likely to use multiple predictors, not just this one, but as I said, churners share many of the signs of busting out, so it’s in our interest to limit the things that make us look like such.
On the other hand, if your inquiries are fairly evenly spread out, it should be more immune to the metric of change in the # month over month. It might still fail the metric of straight up # of inquiries, but overall it seems safer than the alternative (AOR).
I’m a follower of AOR. However, with the recent churning-related shutdowns by Chase, I am going to reconsider this strategy.