By Steven Finlay (auth.)
Read or Download Credit Scoring, Response Modelling and Insurance Rating: A Practical Guide to Forecasting Consumer Behaviour PDF
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Extra info for Credit Scoring, Response Modelling and Insurance Rating: A Practical Guide to Forecasting Consumer Behaviour
If it performs to expectation then the process used to derive it, is arguably, irrelevant. 5 Measures of performance An absolutely crucial part of building a model is evaluating how good the model is; that is, how well the model satisﬁes the business objectives of the organization that commissioned it. For most modelling projects there will be several different performance criteria, but the criteria that receive most attention, by a considerable margin, relate to predictive accuracy, based on the difference between what the model predicted and what actually happened.
Amongst other things, this will include: identifying stakeholders, deﬁning project objectives, producing a project plan, documenting risks and issues, identifying sources of data, agreeing the statistical process that will be applied to Introduction 23 Initiate project Undertake project planning Gather data Clean and prepare data Preliminary data analysis Pre-process data Construct model Evaluate model Model OK? 5 Stages of a model development project 24 Credit Scoring, Response Modelling and Insurance Rating construct the model and setting the criteria by which the success of the project will be judged.
Otherwise, the resulting model will predict something other than the behaviour of interest. Credit scoring, for example, is usually treated as a classiﬁcation problem. The objective is to predict the likelihood that someone who applies for a loan will be a “good” or “bad” payer. However, as we shall discuss in more detail later, what one means by “good payer” and “bad payer” is open to debate. One extreme is to consider someone a good payer only if they have never missed a payment over the entire term of the credit agreement.