At the end of the day, business is about achieving profitability, which is defined as the ability of an enterprise to generate revenues in excess of the costs incurred to produce those revenues and is often measured by a rate of profit or rate of return on investment. Credit underwriters also seek to achieve profitability, and that means avoiding large, unforeseen losses. To maximize profitability, underwriters need to find the optimal balance between premiums charged and risk present.

Unfortunately, as discussed in The Risky Game of Credit Underwriting, underwriters are often working with insufficient, inadequate, or obsolete data so measuring the “risk present” becomes quite a tall order, and many times involves outright guessing. They have no way of knowing where the applicant lies in the ERM – Business Success Matrix. Fortunately, with the advent of a standardized mean to collect and analyze qualitative data, most of these underwriting deficiencies can be overcome. In this post, we’ll discuss how qualitative and quantitative data fit together to form a complete picture of an applicant during the credit underwriting process.

One of the most important components of the Enterprise Risk Management is risk assessment. Without that step, there is no process. However, the assessment of risk focuses principally on qualitative data, an observation that involves subjectivity by the very nature of the measurement. Is it possible that ERM, the process for managing risk, could be used to determine the likelihood of a company’s success or failure based solely upon qualitative data? The answer is no.

In the world of ERM, there is often talk about business failure and determining its likelihood by measuring risk based upon qualitative data. In fact, the term failure risk is often referred to in discussions about the ERM process. The truth is that the study of risk data alone cannot determine the likelihood of failure without due consideration of financial data. In other words, without regard for the results provided by financial statements.

You see, failure may seem all but guaranteed by terrible business systems and controls, which both indicate a high presence of enterprise risk. However, if the company being analyzed has billions of free cash in the bank and routinely makes huge profits, is there a high likelihood of failure? Unless that company is also extremely leveraged, such as was the case with Bear Stearns, the answer is certainly no. Therefore, qualitative data alone cannot determine the likelihood of business failure, which should clear up the incorrect application of the term failure risk. Just as Yin and Yang must coexist by definition, qualitative data must be joined with financial data to make the whole, to complete the risk picture; risk data and financial data are both required to determine the likelihood of business failure.

That being the case, how do we classify the qualitative risk data derived in the ERM process? What is it standing alone? As stated, it is not a determinant of success or failure in itself. However, since every system and process that is not in place, or poorly in place, will harm profitability, qualitative risk data is clearly a determinant of the likelihood of achieving the best results possible. And in the business world that is the likelihood of achieving Maximum Profitability, or the highest level of profitability achievable by an enterprise under ideal conditions. In essence, the qualitative risk data derived in the ERM process determines overall Profitability Risk, that is, the likelihood that an enterprise will not achieve its Maximum Profitability.

Risk by definition is the possibility of suffering loss or harm. In the business world, harm is not a black and white issue, which is suggested by the terms business failure or failure risk. Rather, harm is a spectral issue with a lot of gray area, which is why speaking of a decrease in profitability or profitability risk is more appropriate. Therefore when we talk about the ERM process and focus purely on examining qualitative data, we should be talking about profitability risk of a company, not failure risk. And knowledge about where a company stands in relation to its ability to make a profit is a very valuable piece of underwriting information. Combined with quantitative data, it gives an underwriter a firm grasp on a prospects total potential for business failure or failure risk. The following chart shows the basic relationship between the two:

This relationship between quantitative financial results and qualitative risk data holds true for every industry, however the exact line between profitability risk and financial results will vary.  The bottom line is that both types of data work harmoniously to define the risk of business failure and provide much needed insight into the inner-workings of enterprises.  Those who seek to consider both types of data when making decisions to grant credit or guarantees will be considering the whole of risk.  Like the concept of Yin and Yang, qualitative and quantitative data complement each other and will protect creditors and guarantors if used regularly!  For more information on how qualitative risk information is being standardized, we encourage you to read about the Certified Professional Assessor of Enterprise Risk.