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It would be convenient if one could assume independence of the two main agencies: default and churn.

Although this is likely to be assumed in the interests of keeping things simple, it is unfortunately a doubtful assumption. There may well be a correlation against the bank’s interests in the form of better credit risks finding it easier (then poor credit risks) to re-finance elsewhere on favourable terms. Then, higher churn (earlier closure) may be correlated with lower PD. Full modelling of such a situation would require the joint modelling of default and churn.

Churn is not a ‘risk’ in the Basel meaning(s) but is referred to as such in this post in the sense that it is an uncertain event with unfavourable financial consequence for the bank: opportunity loss of revenue. 

So far the event we’ve been considering as the subject of analysis has been default, with occasional mention of churn.

Default, being progressive, lends itself to analysis of its stages, such as the events of going 30DPD or 60DPD. In addition to default hazard, one can analyse 30DPD hazard and 60DPD hazard. One advantage, especially for monitoring, is that these events occur slightly sooner. A statistical advantage is that these events are more numerous than default events. Given an intuition, or perhaps a model, of how 30DPD and 60DPD profiles relate to default profiles, they could be a useful analytical tool.

That segues into the roll rates discussion AWML.

The relationship however need not be straightforward. For example, there may be a spike of 30DPD or 60DPD at MOB=2 or 3, due to bugs or carelessness with the administration of new re-payment schedules. Most of those would not roll through to default.

“Churn” is used here to refer to accounts closing ahead of schedule for reasons not related to default. Perhaps this is not ideal terminology – I tend to use it because it is short and specific – but other suggestions for common usage would be welcome.

One variation encountered is “closed good”, which will be used later in discussions of “closed goods in” versus “closed goods out” as bases of analysis. This nomenclature is more comfortable than “churneds in/out” would be.

Meaning varies amongst products. For CC, there is no fixed product schedule and churn would normally have the marketing meaning of customers taking their business elsewhere – e.g. “balance transfer” to another CC issuer. This has been a particular concern with aggressive marketing by competitors offering low or zero interest for an introductory period.

For term loans with a fixed principal & interest amortisation schedule, churn could come about from re-financing of a HL or PL with another lender. A similar issue is the early paying down of the loan balance on products that allow this. “Churn” is not a descriptive word for this behaviour – the account may remain open and active but have a much lower loan balance than the bank was expecting. Lower funds at risk means lower earnings for the bank, affecting the profitability model for the product cycle. What would be of particular concern, and likely in practice, would be the correlation between early payment and low PD, i.e. the lowest risk customers reducing in proportion of funds at risk.

As regards default analytics and PD models, churn is a countervailing force to default. If a portfolio has high churn, it will make the default experience look better (if analysed on a “closed goods in” basis AWML). To make a clear analysis of a portfolio it is better to analyse the effects of churn and default separately from each other. For profitability studies, each plays a role.

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