This title seems especially appropriate following the recent Beijing Olympics. But today we are not talking about Chinese culture, we are talking about qualitative data and quantitative data, risk data and financial data, causes for success and causes for failure. What do these have in common? As the Chinese definition goes, they are two complimentary qualities that, when put together, form the whole.

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 , 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 . 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.

(more…)

Technorati Tags: credit underwriting, Enterprise Risk Management, ERM, failure risk, guarantee underwriting, maximum profitability, profitability risk, qualitative data, quantitative data, Risk analysis, risk data, risk premium