In credit rating agencies predictive analytics are widely used and enormous investment is made into the technology that conducts the calculations.
Whether we like it or not we all have a credit rating which has been worked out based on the facts and figures of our personal borrowing, saving and spending habits.
However, in organisations where credit management is not the primary activity, the in-depth analysis of customer credit worthiness does not often receive the same systematic support. This is surprising considering the decisions made in the credit department are nothing less than critical for a company, as they relate to risks affecting working capital – THE most valuable resource in tough economic times.
The credit module found in most ERP systems usually offers a degree of statistical feedback regarding payment performance. The reports produced here though are often of limited value. They can sometimes fail to reflect a true picture as variances caused by invoice disputes for example, can be treated in many different ways.
However, connect to your data from within Business Intelligence like Tableau Software and in minutes you can slice and dice to see the facts exactly in the way you need to. You can establish your own calculations by which to judge historic performance and refine them over time. Furthermore, you can aggregate your information together with data regarding sales, profit and outstanding
quotations to reveal a much wider picture (see Data Aggregation).
If any department in the company needed a crystal ball to do its job, the first one on the list would have to be Risk Management. But until such a time that the aforementioned balls become widely available, predictive analytics is the best way to go. By taking historic information and applying statistical measures to it can generate some very useful facts.
In a parallel way the airlines are very used to doing just this. No shows - travellers who fail to turn up for their flight - are a real problem in an industry where profitability and capacity planning are so closely related. What airlines have found is the very best predictor that a passenger will actually show up for a flight, is the fact that they have ordered a vegetarian meal. What the risk management team needs is a system that helps you to find the vegetarians of the credit world.
To make the distinction between good risk and bad takes experience and a natural intuition, all backed up by insight derived from good data. Using Business Intelligence in credit and risk management gives you the best information so that your team can focus on their skills and not on gathering the data.
As well as being able to carry out this sort of analytical work, Business Intelligence is also very useful in keeping teams up to date with performance. By combining important details in the form of graphs, charts and table you create a dashboard that can then be distributed throughout the team. Showing how everyone is doing against the target can be a great source of motivation.