Credit scorecards are a very popular approach to the quantitative representation of the probability that clients will be prone to demonstrate some defined behavior, like, for instance, loan default, bankruptcy, or payment without delinquency. Clients are described by a set of attributes that are characterized with the specific partial scores that represent the contribution of the attributes to the final score - the higher score the more tendency to demonstrate the target behavior. The partial scores can be learned from the historical data that connect the clients' features with the target behavior and the commonly used technique, in this case, is Logistics regression. The coefficients of the Logistic Regression model can be transformed to partial scores with scaling so that they reflect the impact of the separate attribute on the final decision and lead to the expected Scores ranges.
There are two popular approaches for the scaling of the Logistics Regression coefficients ($\beta_i$ - LogReg coefficient for the variable $X_i$, $\alpha$ - LogReg intercept, n - number of the independent variables $X_i$):
Min-max scaling - transform the LogReg coefficients to the partial scores so that the final clients' scores belong to the min-max range
$$ Factor = \frac{(ExpectedScore_{max} - ExpectedScore_{min})}{(ModelScore_{max} - ModelScore_{min})} $$
$$ Offset = ExpectedScore_{min}-ModelScore_{min} $$
$$ Score_i = Offset/n - (\beta_i +\alpha /n) \times Factor $$
Odds-based scaling - transform the LogReg coefficients to the partial scores so that the final clients' scores reflect the credit default odds. This method depends on three parameters - target odds, target score and points to double the odds (pdo): example - target score of 600 to mean a 50 (target odds) to 1 odds of the good customer to bad, and an increase of 20 means a doubling odds)
$$ Factor = pdo/ln(2) $$
$$ Offset = Target Score — (Factor × ln(Target Odds)) $$
$$ Score_i = Offset/n - (\beta_i +\alpha /n)\times Factor $$
The scaling procedure can be extended with WOE correction - this approach allows to consider the dependencies in the novel labeled data:
$$ Score_i = Offset/n + (\beta_i \times WOE_i+\alpha /n) \times Factor $$
Bricks → Machine Learning → Scorecard
Bricks → Analytics → Credit Scoring → Scorecard
Bricks → Use Cases → Credit Scoring → Credit Scoring Model → Scorecard
Scaling
The type of final scores representation. There are two strategies are available - Min-Max scaling and Odds scaling
Min Score
The minimal value of the client score is expected after the Scorecard brick applying to the trained Logistic regression model. It is expected that the worst situation is described with this score
Max Score
The maximal value of the client score is expected after the Scorecard brick applying to the trained Logistic regression model. It is expected that the best situation is described with this score
Target odds and Target Score
Odds-based scaling parameters - "target score" defines the client with "target odds" to 1 odds ****of the good customer to bad.
Points to double the odds
Score increasing value when good/bad odds are doubled
WoE correction
The binary flag for the adjustment of the score based on WoE considering
Target
A binary variable that is used as a target variable in a binary classification problem. The weight of evidence of the separate attributes is calculated with respect to the specified target. The target variable should be present in the input dataset and takes two values - (0, 1).
Columns
List of possible columns for selection. It is possible to choose several columns for filtering by clicking on the '+' button in the brick settings and specifying the way of their processing:
Remove all except selected
The binary flag, which determines the behavior in the context of the selected columns