INDIVIDUAL CREDIT SCORE

CREDIT SCORE

• Credit score is a number representing the creditworthiness of an individual. 
• Credit bureaus collect credit information of borrowing and repayment and sell it to creditors for a fee.
• Credit score is primarily based on information sourced from a credit bureau.
• There is no international credit score.  Lenders assesses credit worthiness specific to different country and region.

SCORING COMPONENTS – FINANCIAL AND DEMOGRAPHICS

• Number of accounts Active / Inactive / Current / Default
• Length of credit history
• Credit mixMortgage, auto loan, credit card, personal loan
• Repayment history
• Age
• Occupation Self employed / Full time / Half time / Unemployed / Retired
• Years of employment 
• Income
• Residential status Rent / Ownership

SCORECARD DEVELOPMENT METHODS

• Expert scorecard Human subject matter expert
• Classical methods Generalized Linear Model (GLM)
• ML models Random Forest, Boosting
• Deep learning Neural network for classification

CASE STUDY 1

• A company in the UK wanted a scorecard for its customers applying for auto loan.
• The profile of the customers was sub prime.  
• The vehicles were primarily used ones.  
• The lender wanted to improve alignment between underwriter rules and the score.
• They also wanted to compare two different bureau data in terms of quality and coverage.

Solution

• Customer classification using domain knowledge and statistical methods like decision tree and cluster analysis.
• Multiple scorecards each rescaled to have similar odds
• Scorecard as a linear function for easy integration with loan origination system
• Revision of underwriting rule and corporate reporting system to de-duplicate variables across model & underwriting rule engine

Objective

• Application scorecard for auto loans for subprime customers

Benefits

• Multiple scorecards
• superior to a single scorecard
• Recommendation on underwriting
• ensured the exclusion of model variables from underwriting rule
• Consistent decision
• from adaptation of a  mathematical model

CASE STUDY 2

• A major Telecom company in Malaysia wanted to create a score for its customers.
• The company was using a bureau score which, was developed by using a database of 1 million customers.
• Telco default was disconnected/suspended 6 months ever.
• A tailor-made scorecard for Telco customers was thought to have better discriminatory power.

Solution

• Characteristics selected from broad areas of auto finance, credit card, personal loan, months in arrears, check bounce, application/approval amount, etc.
• Developed scorecards using:
  1. Logistic Regression (LR)
  2. Machine Learning (ML) methods
     • Random Forest (RF)
     • Extreme Gradient Boosting (XGB)
• Interpretation of ML models were made possible by the use of Local Interpretable Model-Agnostic Explanation (LIME).

CASE STUDY 3

• A company in Malaysia wanted to create a customized score for small-ticket loans.
• The company used the bureau score which was developed using Logistic regression.
• It was felt that a generic bureau score was not discriminating enough for this segment.
• A tailor-made scorecard and ML model for evaluating credit risk were required.

Solution

• Characteristics selected primarily from the history of credit card and personal finance of which variables from payment and MOB were the most important variables
• Developed scorecards using:
  1. Logistic Regression (LR)
  2. Extreme Gradient Boosting (XGB)
• Interpretation of ML models were made possible by the use of Local Interpretable Model-Agnostic Explanation (LIME).

Objective

• A scorecard with bureau data for small-ticket personal loan

Benefits

• Satisfactory LR and ML scores
• both aided in decision-making
• Model worked for mid level loans
• ensuring application of the same scorecard for a different segment
• Reasons for ML scores
• for each customer added transparency to the black box model