As you may know, business risk models have not fundamentally changed over the past 40 years. The famed Altman Z-score model, first published in 1968 by Edward Altman, is still being used as a pillar in the area of modeling bankruptcy. Why? Well, because risk models are typically founded on basic financial information such as working capital, total assets, retained earnings, EBIT, equity, sales, and similar financial statistics that reflect fundamental measurements of company health. Since the importance of these basic financial barometers hasn’t changed over time, the models that employ them haven’t needed to change either. It is true that improvements in risk model performance can be made by incorporating payment patterns, however this is more suitable for internal customer scoring models, as finding enough reliable and ongoing payment data for an external risk model build and score is difficult indeed!
Having been in the business risk and opportunity-modeling arena for many years, I’ve come to the conclusion that the greatest weakness in business data modeling is quite simply the age of the data. There is no doubt that a downswing in EBIT spells bad news; but by the time that is recognized in a financial report, it’s very late in the game, and no modeling technique can overcome the limitations of old data. In my search for a better source of leading indicators, I naturally gravitated to the internet. After all, the Internet offers an unparalleled rich, dynamic source of data in both quantitative (e.g. financial reports) and qualitative (e.g. sentiment) form, and many of these are inherently powerful leading indicators of both risk and opportunity.
Not coincidentally, statistical package developers such as SAS and SPSS have already launched applications that combine text mining and analytics. However, for many companies, it will be preferable to gather the data as a separate process, and then integrate it into their modeling/decisioning processes. Recently, I’ve found that incorporation of web data can improve the accuracy/timeliness of risk-based decisions by as much as 20%; even larger benefits can be expected in the area of market potential analytics.
Stay tuned for my upcoming musings on this topic. Part 2: Using Web Mined Data to Enhance the Performance of Business Risk and Opportunity Models
Please contact me with any questions or comments. I can be reached by commenting on the blog, or via email at Steve (at) digitaltrowel.com
Looking forward to an active dialogue.