Statistical

models Opposed to approach of Judgmental models is the one of statistical

modeling, which advocates superiority of quantitative data in establishing

underlying causal relationship between the probability of default and cause

factors. Statistical models are governed by statistical methods, considering

many factors simultaneously, thus calculating and analyzing multivariate

correlation in order to identify most powerful factors and produce

statistically derived weights to be used in consequent scoring model.

Use

of statistical models in collection process advocates the emphasis placed on

inherent risk characteristic of the borrower as opposed to aging items. Why is

statistical model preferential to judgmental in such a case? Reason is that the

latter informs on quality of the risk separating lowest risk accounts from

highest ones, while statistical model quantifies the risk by informing you on

the probability of default and, therefore, associated expected loss, i.e. value

of the risk (Driving Internal Collection Results With Statistical-based Credit

Scoring, 2010).

One

of the most important and prominent contributions made to the field was the

observation by Beaver (1967) that there are several financial ratios, which

differ significantly between failed and nonfailed firms, in particular cash

flow/net worth and debt/net worth (Falkenstein, Boral, & Carty,

RiskCalc(TM) For Private Companies: Moody’s Default Model, 2000). In short,

differences in such ratios for viable and bankrupt companies increase as time

to default shortens – as failure neared, firms became more dissimilar.