Part 1 of this article series discussed why and how Predictive Warning Systems should and can be used to mitigate risk. Now we turn to look at how Predictive Warning System’s can be built and implemented to achieve lasting and reliable results.
You have an amount of past situations which either resulted in a bad debt or not. About these past cases, snapshots are taken at selected time points; at the moment of each snapshot there had been some particular recent history which we can paraphrase with an appropriate set of key performance indicators, also called “features”. Some features are likely to be applicable in any scenario, like the slope of the upward or downward trends in the 30+ and 90+ unpaid invoices. The fact, if there were at all any invoices going into 30+ or 90+? How the customer historically reacted on calls to pay? Whether the average outstanding of the recent months was considerably larger than the average amount a year ago?
In case of a B2C scenario you can exploit features related to the mindset of your customers, like what is their default payment method and in contrast to that what mix of payment methods they are actually using. In case of a telco provider, where customers could use a prepaid balance for using some services, a relationship was found between reliability to pay on time and playing lottery on the phone. In a B2B scenario, the amount of credit or debit notes, and their ratio to the overall balance was one useful factor.
There may be lots of other features which put a light on your risk exposure, all from a slightly different angle. If you have a rich enough dataset then a properly selected algorithm will be able to find the subtle differences between feature combinations which are rather positive or rather negative. And this is what we want – the desired model itself.
At the moment when you want to evaluate your momentary outstandings, the work to be done is much more simple. You just need to get your system to calculate all the needed features from the latest data. The actual features are substituted into the model – and there you are: you get your current risk assessment. The model building and scoring mechanism.
The volume of the final savings will depend on your business circumstances. But to mention a bright example, a department belonging to a large international telecommunication company has been able to halve its 90+ days outstanding volume within a few months with the help of a monthly run bad debt PWS, and the improvement went further in the longer run.
Invest energy into the below issues and you will establish a long-lasting and reliable bad debt PWS for yourself.
This article was written by Tamas Molnar , an advanced analytics expert who has successfully delivered analytical solutions in a number of industries to include telcomms, retail and banking, as well as across several domains (enterprise and residential sales, operations, marketing, finance and HR).