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What is scoring and why you need it


Scorecard

To score customers you need a scorecard.


Scorecard is a mathematical model represented as a set of weights assigned to customer’s characteristics that affect his creditworthiness or any other target behavior modeled by the scorecard.


To create statistically-based scorecard you need to apply statistical (predictive analytics) method to your historical data (data about your past and current customers).


The most widely used statistical method for scorecard development is logistic regression.






Plug&Score is the most easy-to-use and the fastest to integrate scoring system.

For more complex and versatile needs of larger credit institutions we recommend Scorto™:
Acquaintance with Scoring
What is scoring and why you need it
Scoring
Scorecard
Cut-off, odds and risk groups
What scoring can bring you
Profitability of Operational Decisions
Decreased Decision Time
Agility and Reduced Labor Costs
Types of Scoring
Judgmental vs. Statistical Scorecard
What you need to start using scoring
Scorecard Development
Scorecard Development Stages
Historical Data Preparation
Training & Validation Datasets
Binning
Characteristics Selection Using
Information Value
Logistic Regression for Scorecard Calculation
Automatic Variables Selection with Stepwise Methods
Basic Scorecard Quality Assessment
Reject Inference
Scorecard Scaling
Scorecard Validation
Gini & ROC Curve
Kolmogorov-Smirnov Curve
Risk Segments
Strategy Curve
Approval vs "Bad" Rate
"Good" and "Bad" distribution
Cumulative Event Rates
Chi-squared Scorecard Stability Test
Score Points Distribution
Scorecard Implementation & Deployment
Verifying Borrowers Characteristics Stability
Scorecard Correction (if required)
Setting Cut-off
Policy Rules Definition
Scorecard Deployment
Champion-Challenger Deployment Approach
Advanced How-to
How to Improve Scorecard Accuracy
How to Apply Reject Inference Methods