Credit (Risk) Management has been out there… forever, it feels. Or at least since 1841.
The four V’s of Big Data
Big Data is a much newer term. I tend to think that it’s a new name for something that people have been doing for a long time too: data analysis to create meaningful insights. However the characteristics of the data analysis changed with the proliferation of information, with what feels like an exponential growth of the volume, variety, velocity, and veracity of data; the so-called “four V’s of Big Data”. And this holds also for credit management.
Question: Have Predictive Data Such As Credit Risk Scores Become A Commodity?
It’s no longer difficult to find someone who can predict anything for almost nothing, including the failure of your potential clients and suppliers. But make no mistake, qualitative predictive scores are not a commodity.
How can you judge the quality of such predictive score? No hard criteria exist, except for waiting and doing a post-mortem analysis. But this is not an option, because you do not want to risk your company’s P&L, do you?
Answer: No. Effective Credit Risk Scores Still Require High Quality Data
My answer is: like with anything in life, there are no free meals. Quality comes with a price, and a long term investment in data collection, in data quality (timeliness, completeness, correctness,…) and in creating insights is required. Because predictive insights that are based on incomplete data, on incorrect data, on untimely data or on data that for any other reason has low quality, are very expensive: they will not only result in you not predicting real failures, but they will also result in too many false positives: you will predict failures of (and thus not do business with) companies that are financially healthy.
And this is my lesson learned also for credit management: data quality is the key to success, and predictive insights to manage your credit risk are of high value. Whose data quality do you trust most?
Note: This article has been published on August 25th 2017, in Dutch, on Krediet Rapport Opvragen.
Turning Data into Business: Data Quality vs. Data Quantity
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