Knowing when the majority of the loans default, can give you a good idea on how well your portfolio or some portion of the platform portfolio is performing.
In addition, if you know what proportion of loans default in a 6-month period after issuing and that the proportions have been relatively stable over time, you can make some rough estimations about your future default rates also.
The estimations might not be exactly scientifically accurate, but they’re certainly a whole lot more accurate than no estimations at all.
To shed some like to this topic, I’ve done a few analyses on the loan dataset provided by Bondora and will highlight some results below.
When do defaults happen?
First I decided to establish some sort of a baseline by looking at the entire dataset to see when do loans usually default.
Same information split into daily proportions of defaults.
About 50% of defaults have happened within the first 6 months and the biggest jump in defaults seems to happen in the first 120 days.
Defaults for different markets
From here, I wanted to see if there is any difference between markets. For this I have used the loans issued in 2013 and put the defaults into certain ranges to get a clearer image of what’s going on. This means that Slovakia is not included, as there were no loans issued in 2013.
There doesn’t seem to be much difference between countries for the proportions of defaults. For Estonian loans it seems like more defaults happen later on, but actually it’s just simply the fact that Finnish market was opened only at the end of July and Spanish market in October.
As time goes on, there’ll be some defaults at the 480 days point and some at 600 for FIN and ESP loans too and the graphs will likely look very much the same.
Defaults for different years
Another thing to look at is whether the proportions have changed in time and how. Again I split up the periods into ranges and put the results on the interesting graph below.
Some conclusions to make on this:
- Year after year, the defaults seem to be happening later on in the loan term. My guess would be that this is the result of more stricter underwriting rules and better fraud checks. For example, initially it was possible to apply even with current payment problems.
- The long tail really adds up after 2009. This is not some huge increase at some certain date, but small amounts of defaults every now and then which is to be expected (people lose their jobs or get in difficulties).
- Even though 2013 seems like a large increase in the proportion of initial defaults, then do keep in mind that a large portion of the loans haven’t even reached the 360 day deadline so far and the later defaults are still to come in. The pattern seems relatively same as for previous years.
Year after year the pattern seems to be essentially same with some tendency of proportionally more loans defaulting after a longer period of time.
A question of some interest now was to check whether there’s a difference in when a loan was issued. We know that there’s some seasonality when more defaults happen so would be interesting to see if there’s any differences based on the month when a loan is issued.
I ended up with this a bit crowded graph here (I know, it’s a bit difficult to figure out the months, but that’s the best I could get):
There seems to be two patterns that are separate from other months:
- Loans issued in the summer months (April-June) seem to have largest proportion of defaults within the first 120 days (up to 4 months since issuing).
- Loans issued in the winter months (October-January) seem to be defaulting mostly within the 120-240 day range (4-8 months since issuing).
If someone cares to go more deeply into the data, I think it would be very interesting to see possible reasons behind this. If you have an idea why this is in such a way, let us know in the comments.
Credit groups and default proportions
I did look at the numbers for different credit groups too to see if perhaps this explains some of the differences, but not really.
There are some differences, but with the exception of credit score 500 and perhaps the 360 day part for 600-700, but nothing that clearly would say that “this is the cause for the differences”.
I guess this is the place for someone to fire up their SPSS and come up with an answer.
So, what’s your take on this?
If you’d like to receive the dataset I used for my analyses with all the data and graphs, then subscribe to the list below: