Creating Documentation for Credit Scoring Model

For a financial company, a credit scoring model is an important way to assess the creditworthiness of customers. To keep the model accurate and reliable, you need clear, complete documentation.

Goals for a Model Documentation from a Management Perspective

• Ensure the model is transparent and easy for stakeholders to understand.
• Provide a clear view of the model's performance and limitations.
• Describe the model's assumptions, methods, and results, including possible sources of bias or error.

Documenting Sample Selection

The first step is to document the sampling methods used for training and validation, along with any out-of-time validation performed. This information matters because it shows how the model performs and whether it represents the population it is applied to.

Sampling Methods Used for Building a Predictive Model

1. Simple random sampling: A basic method where each member of the population has an equal chance of being selected. It is often used when the goal is a representative sample.
2. Stratified sampling: This method divides the population into subgroups based on specific characteristics, then selects a random sample from each subgroup. It is useful when you need the sample to reflect those characteristics.
3. Cluster sampling: This method divides the population into groups, then selects a random sample of groups. All members of the selected groups are included. It is often used when listing every population member is impractical.
4. Systematic sampling: This method starts from a random point, then selects every nth member of the population. It is often used to create an evenly spaced sample.
5. Convenience sampling: This method selects population members based on availability or accessibility. It can speed up data collection, but it can also introduce bias if the sample is not random.

Evaluating the Stability and Performance of a Predictive Model

Next, check model stability, both in overall predictive power and in month-to-month consistency. You can do this with techniques such as the ROC curve, which helps identify potential issues. You should also review the stability of the information value of the attributes used in the model to confirm they deliver consistent results.

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Metrics for Evaluating the Performance of a Predictive Model

1. Accuracy: Measures the percentage of correct predictions made by the model. It is a common metric for evaluating predictive performance.
2. Precision: Measures the share of positive predictions that are actually correct. It is often used with accuracy to give a fuller view of performance.
3. Recall: Measures the share of actual positive cases the model correctly predicts. It is useful when identifying all positive cases matters, even if false positives increase.
4. F1 score: A weighted average of precision and recall. It is often used as a single summary metric.
5. AUC-ROC: Measures how well a model distinguishes between positive and negative cases. It is calculated by plotting the true positive rate against the false positive rate at different thresholds, and is often used for binary classification models.
6. Confusion matrix: Shows the number of true positive, true negative, false positive, and false negative predictions. It helps identify where a model performs poorly.
7. Logarithmic loss: Measures how well a model predicts the probability of an outcome. It is often used for models that output probabilities, such as binary classification models.

Impact of Seasonality

Another factor to document is any seasonality that may affect the sample selected for the model. These effects can materially change model performance, so they should be recorded and accounted for in the documentation.

Examples of potential seasonalities that may affect a predictive model:

1. Seasonal changes in demand for certain products or services, such as higher demand for travel insurance in summer
2. Seasonal changes in economic conditions, such as higher unemployment in winter
3. Seasonal variation in weather patterns, such as a higher likelihood of natural disasters at certain times of year
4. Seasonal changes in consumer behavior, such as higher spending during the holiday season

Evaluating and Documenting Model Biases

Any potential model bias should also be reviewed and documented. If the model replaces an existing model, comparing grade changes with a crosstab can help assess performance and effectiveness.

Five potential biases to consider in a predictive model

1. Sampling bias: This happens when the sample used to develop the model does not represent the population it is meant to be applied to.
2. Selection bias: This happens when the sample used to develop the model is not randomly selected and may be shaped by factors such as data availability or the modeler's preferences.
3. Confirmation bias: This happens when the modeler focuses only on data that supports existing beliefs or hypotheses, while ignoring conflicting data.
4. Overfitting: This happens when the model is too complex and trained too closely on available data, which leads to weak generalization.
5. Underfitting: This happens when the model is too simple and not trained on enough data, which leads to weak predictive power.

Conclusion

Clear documentation for your credit scoring model is important if you want consistent, reliable results. By documenting the key parts of model development and validation, you make the model easier to assess, explain, and defend.