Following the comments on indeterminate, it is timely to introduce some shorthand notation that will help in discussions that follow.
The issue is the point-in-time default definition. Your default definition should in the first instance produce a decision at every point in time as to whether the account is in default or not. The set of possible points in time is determined by the time granularity of your data systems. A typical situation would be monthly data for CC with default flagged for >=90DPD assuming the outstanding balance exceeds some materiality parameter(s).
But why point-in-time default definition? Because this is not the final default story; the re-ageing logic still needs to be superimposed. Re-ageing involves an extension of the point-in-time default definition to the concept of a default episode, which has temporal extent i.e. it is a time window having a start date and an end date. Today’s post, however, covers only the point-in-time default issue, and the qualifier “point-in-time” will be left out to avoid clutter.
Default history for any particular account can be summarised by the string of consecutive default statuses: for example GGGGGGIIIBBG shows the account was ‘good’ for the first 6 months, ‘indeterminate’ for the following three months and then ‘bad’ for two months but then ‘good’ again in the 12th month. These 12 months could be the first 12 months since the account opened, if you are doing longitudinal analysis, or it could be the 12 months of a cross sectional analysis, in which case it might represent something like MOB 33-44.
The definition details of ‘bad’ and ‘good’ will be particular to each institution and product, but status codes that I have found useful include:
- B = Bad, i.e. point-in-time in default
- I = Indeterminate. Optional status, not all situations require that one should need to distinguish these from G and B i.e. ask the question: how does ‘I’ differ from ‘G’?
- R = in recoveries
- C = in collections
- W = has been written off
- G = Good, i.e. not bad nor any other status with a higher precedence
- U = Undrawn. This can apply to loan accounts that have been set-up and are open on the books, but where the capital has not been drawn down yet. For HLs there can be a few months delay if there are hold-ups in transfer. In the meantime, they appear as accounts with zero balance outstanding. This only applies when this situation happens at the beginning of an account’s history, i.e. not for zero balance accounts that can occur later. Undrawn does not apply to some products such as CC because they are only activated when the first transaction is made.
- D = Dormant. It may be useful to identify accounts that appear to be dormant, i.e. have returned to a zero balance and there is no customer initiated activity for a long time. Because of Basel treatment, but also for commercial reasons, the bank may want to identify these and do something about them.
2 comments
28 April, 2008 at 09:55
Guy
Clive, great coverage – commended – I’m now an avid reader. Just on your G,B and the rest article, you discussed an important and relevant issue which is what I call ‘Bad Survivors’. The reason these are important is that they can be quite profitable accounts, particularly with credit cards (assuming operational unit costs held constant across risk bands…. intuitively this is not the case, but exponents of the ‘contribution to fixed costs’ argument would discard as not relevant anyway). Another piece I think is important is the treatment of Closed Goods or Attrited Goods. It seems a dilemma for scorecard builders as to whether these should be included in the build or not (attribute correlations are impacted, but degree is debatable as it is varied) – either way though, in my opinion, it is critically important (in operationalising Basel II) that the business understands whether Good Closers were in or out. Reason being, if a cut off was set to drive a RAROC outcome by shooting for say a cumulative G:B of 12:1 on a scorecard built with Good Closers included (treated as Goods), then Attrition (or changes to) would impact the RAROC outcome (up or down). If the model was built with Good Closers excluded (ie. smaller denominator), then PDs would be more or less ‘worst case’ in the sense that the profit generated by the Good Closures would be unaccounted cream for the taking (assuming out acquisition costs). In my mind, to do RAROC right, Good Closers should be included in PD model build, but an Attrition emergence model should be augmented to the calculation, AND risk based scalars applied to all fixed unit costs (ABCs). Interested to read your feedback on this.
Cheers
GM
29 April, 2008 at 00:22
Clive
Guy, thanks for your contribution and insights on two interesting points.
#1: ‘bad survivors’: presumably any extent to which a defaulted account can continue servicing debt adds to profit, even if eventual write-off is not avoided. IIUC you’re pointing out that well-managed collections/recovery procedures are a profit centre for banks. Perhaps there’s a danger of Basel language tempting analysts into linear thinking PD->default->EAD->Loss as one-way traffic.
#2: We’re on the same page about closed goods and I’m hoping to illustrate with the benefit of a worked example – watch this space in a week or two and let’s resume the discussion.
Cheers,
Clive