The DSCR divides this cash flow amount by the debt service (both principal and interest payments on all loans) that will be required to be met.
Incorporating certain soft (qualitative) data in a risk model is particularly demanding, however successful implementation eliminates human error and reduces potential for misuse.
Even so, in the granting of credit to corporate customers, many banks continue to rely primarily on their traditional expert system for evaluating potential borrowers.
The univariate approach enables an analyst starting an inquiry to determine whether a particular ratio for a potential borrower differs markedly from the norm for its industry.
However, some univariate measures – such as the specific industry group, public versus private company, and region – are categorical rather than ratio-level values.
Although univariate models are still in use today in many banks, most academics and an increasing number of practitioners seem to disapprove of ratio analysis as a means of assessing the performance of a business enterprise.
Many respected theorists downgrade the arbitrary rules of thumb (such as company ratio comparisons) that are widely used by practitioners and favor instead the application of more rigorous statistical techniques.
Fuzzy logic and neural networks are examples of novel methods of developing credit scoring expert systems that deliver greater accuracy in estimates of future performance of a business enterprise.
This path can lead to roles such as team leader or Chief Risk Officer (CRO) after gaining substantial experience in the sector.
Credit analysts' deep understanding of client finances and risks uniquely positions them for roles like relationship manager in corporate banking.