Statistical data in establishing underlying causal relationshipStatistical data in establishing underlying causal relationship

models opposed to approach of Judgmental models is the one of statistical modelling,
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 analysing multivariate correlation
in order to identify most powerful factors and produce statistically derived
weights to be used in consequent scoring model.

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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. 

One step
further was taken by Altman (1968), who reasoned it optimal to create a
multivariate model with several such important explanatory variables that had
low correlation as compared to a single variable model in predicting solvency
of a firm. Quantitative data does not do the work if is used as individual
ratios, as that would constrain the analyst to compare the stats sequentially
and likely end up with ambiguous result. Multivariate model is therefore useful
tool in this field. Altman was the first one to use a statistical model to
predict default probabilities of companies, estimating the Z-score model
through the application of discriminant analysis, which subsequently underwent
several refinements and became a benchmark in academic literature. Although
popular, Z-score failed to establish itself as the ultimate tool in determining
credit quality of a borrower. Reason is that the model does not perform well,
scoring roughly equivalent to a simple univariate ratio benchmark as
liabilities/assets (Falkenstein, Boral, & Carty, RiskCalc(TM) For Private
Companies: Moody’s Default Model, 2000).