Credit scoring.

The credit score is a numeric expression measuring people’s creditworthiness. These scores and other data like the expected approval rates, profit, churn, and losses, are then used to support decision making. Credit scoring means applying a statistical model to assign a risk score to a credit application. It is a form of Artificial Intelligence, that assesses the likelihood of a customer defaulting on a credit obligation, becoming delinquent or insolvent. A modelling technique used for implementing credit scoring is the Credit Scorecard model. It is used to provide direct input for risk-based pricing.

How to Run the Demo.

  • Step 1 : Download the sample test data - demo_credit_scoring_test_data.xls
  • Step 2 : Browse the downloaded file in your system.
  • Step 3 : Click on “Run Model”, and view the downloaded results in csv file that shows the credit scores of the users.

The Credit Scorecard Demo.

The scorecard created is based on attributes of each customer like their Age, Number of Time 30-59 Days Past Due Not Worse, Revolving Utilization of Unsecured Lines, Number of Times 90 Days Late and similar. The data from a bank or financial firm consisting these customer details are uploaded. The machine learning model learns on the “Weight of Evidence value” and the “Information Value” (based on WoE), calculated from the banking data. The machine learning model coefficients are then used to scale the scorecard- making the scorecard conform to a particular range of scores. The final credit score, a simple sum of individual core values of each feature in the data, can be taken from the scorecard which can be downloaded into comma-separated value (CSV) file.

The benefits of AI-based Credit scoring: .