Statistical associated expected loss, i.e. value of the risk

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). 

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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.  

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