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4.2.5 Credit scores – the outcome

How did you do? What order of credit worthiness did you put our four borrowers in?

First you should note that the credit scoring models employed do vary from one institution to another. This means that the scores they generate can vary accordingly when applied to the same applicant.

The key things that score well though, besides having a good or high level of household income, are factors that infer stability and orderliness in the financial affairs of applicants. So stability of employment and in domestic residence scores well. Being an owner-occupier does too – even when the borrowing sought is not going to be secured against the property. Being older helps as you may be able to demonstrate a record of credit worthiness that stretches back over decades. Having fewer children also helps as it implies lower household expenditure commitments. Being in a professional job also helps as this is likely to reduce the risk of having periodic gaps in employment that reduce household income. Having a bank account and credit cards also help – particularly as they demonstrate that credit scoring tests have been ‘passed’ on previous occasions. Being married is positive for your score – being divorced is negative given, again, the inference of these when it comes to the stability and robustness of household finances.

By contrast being young, having a large number of dependants, living in rented accommodation – particularly if there is a record of moving home regularly – having a poor employment record and limited existing access to banking facilities are generally bad news when it comes to a credit score. Such factors provide no comfort about the solidity and orderliness of household finances, implying that lending to such applicants is risky. Additionally it is less likely that such applicants would have been able to build up a long term record of proven credit worthiness. Lending to those with a poor credit score may still take place, but the interest rate charged may be higher – perhaps materially – to reflect the risks involved.

The assessments of credit rating agencies are, though, not impressionistic. They are underpinned by data analysis of statistical relationships between aspects of social status and evidence of credit defaults.

So our model produced the following credit league table ranking:

  1. Rajeev
  2. Jo
  3. Bill
  4. Dave.
Figure 10