Validation of Scoring Model

Don't let your credit models sleep on accuracy.

Test, adjust, and perfect your models to ensure they stay valid in an ever-changing world.

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Find issues proactively,
resolve them ahead

As regulations change, economies shift, and customer habits evolve, your scoring and rating models can lose relevancy. Our credit model validation toolkit keeps them accurate, giving you a clear picture of your customers’ creditworthiness on an easy-to-read results dashboard.

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Deep-dive analysis

Display detailed results of each test in dedicated data tables and graphs.

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Limits configuration

Use an intuitive custom traffic lights system to easily read testing results.

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Observation period

Set the desired period for credit model validation, aligned with one of the Probability of Default definition.

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Scoring grades & PD configuration

Define scoring model value intervals attributed to each scoring grade and the corresponding Probability of Default.

Refine your scoring models, one insight at a time

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Over the years, your scoring models will lose accuracy as external factors change. Additionally, your data quality may decline while new insights emerge that can be used to reevaluate and improve your scoring models.

With ITSCREDIT, you gain a flexible credit model validation tool that adapts effortlessly to your data and parameters. Run a detailed analysis of your credit rating and scoring models, breaking down into key dimensions for deeper insights and a more granular understanding.

Key aspects of smarter credit scoring validation

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Model concentration

Check for scoring grades that dominate your portfolio, which can throw off the balance of risk. Find grades with insufficient statistical significance to reliably estimate default parameters.

Distribution by scoring grade
Herfindahl index
Maximum/ minimum exposure
Concentration within each model variable
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Model stability

Compare and track changes in portfolio distribution across two chosen periods, at portfolio or variable level. Highlight notable variations and proactively identify potential changes in risk characteristics of a specific portfolio not reflected in your current model.

Variation of distribution by scoring grade
Stability of the average PD between time periods
Population Stability Index (PSI)
Evolution of average PD over time
Matrix of scoring grade transitions
Variable Stability Index (SI)
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Discriminatory power

See how well your scoring model performs overall and how each variable stacks up. Easily spot when a model or some of its variables aren't effectively distinguishing between good and bad clients.

Receiver Operating Characteristic (ROC)
Kolmogorov-Smirnov (test)
Information Value (variable level)
Correlation factors matrix
Default rate monotonicity testing
Relationship with macroeconomic variables
Top performant variables
Credit performance by analyst
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Callibration and backtesting

Check if your model's Probabilities of Default match the recent default behavior. Ensure the portfolio's risk is captured accurately, so you can spot mismatches between observed default rates and PDs for each scoring grade and update them when needed.

Comparison between PD and default rates
Confidence interval limits
Statistical backtesting
Heterogeneity testing
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Data quality

Identify potential problems or issues with data used for scoring calculations to avoid incorrect risk assessments and overstating or understating the actual risk.

Missing rates
Nulls and non-specified categories
Boxplot graphical representation
Uniqueness and outliers testing
Consistency tests

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