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INTRODUCTION
In the 50 years that credit scoring has been around, it has placed
itself at the heart of lending institutions decision-making. Slowly at
first, with a few companies testing the concept, then more rapidly as
the concepts became more widely accepted. With wider acceptance came
greater depth of information and improved technology. Scorecards, look
like they do today because of the limited technology of the past;
scorecards had to be integers and were additive so that operators could
calculate the score and compare the result to a pass mark in their
heads. By the start of the 1980’s the US and UK were developing consumer
credit bureaux that could capture lenders information and summarise it
to aid future lending decisions. Account information sharing
kick-started credit scoring as models became more complicated,
sophisticated and predictive. Automation of credit bureau links enabled
applicant information to be assessed rapidly and consistently. Gradually
businesses overcame their reliance on subjective decision-making as the
systematic, consistent approach was shown to out perform human
judgement.
To assist the transition, the decision engines also improved. They moved
from rule-based, hard coded software to highly parameterized flexible
solutions that empowered the risk management teams. Strategy software
developed so that decisions could be given extra dimensions.
Applications could be segmented for different treatment and decisions
progressed from one-dimensional accept or decline, to include line
assignment, product features and risk based pricing.
Initially the credit bureau was seen as an expense to be saved and many
early scorecards were designed with two stages. Application score
followed by a final score. Applications failing the application score
would not progress to the credit bureau thereby saving the cost of a
search. The power of the bureau data combined with the ability to tailor
offers to risk segments, has resulted in an about face: the credit
bureau search is now often the first stage after sufficient identity
information has been captured. Some organisations now reject applicants
who don’t meet credit bureau criteria, thus saving processing cost, or
determine the second stage questions based upon the bureau outcome.
The 1990’s saw the rapid spread of scoring as companies recovered from
the Recession, realising that objective decision-making reduced
operational risk, whilst statistical tools provided the control
management needed to ensure the appropriate level of risk was being
taken. Regulators also recognised that scoring provided an evaluation of
the risk being taken by a lender and have now determined international
rules for determining the level of capital that a bank should hold
relative to the potential losses. As a result, what started as a trickle
in the 1960’s has become a flood today with the mortgage lenders and
others joining the approach to underwriting and portfolio management
what was started mainly by the mail order and store credit companies.
The greater the discrimination of risk, the lower the capital
requirement and so the pressure is on to find new techniques and data to
improve the models. In response, credit bureaus have deepened their data
and models and the incorporation of these in lenders’ scorecards is
growing. Where a single bureau reference may be taken, some lenders now
find benefit in taking multiple searches from multiple bureaus.
This book covers the practical issues of building good application and
behavioural scorecards. Many assumptions are made during a development
and it is imperative that both the developer and the organisation
appreciate what approaches have been used and what the implications are.
The growing importance of and reliance on scorecards, means that the
models must be robust and practical. A scorecard that appears
statistically to be highly discriminatory must deliver those benefits.
Time and time again you will read that operational practicality and the
strategic use of the model is more important than the new technology
used to build the scorecard.
Peter Constance
Pancredit
May 2006 |