Credit Scoring in Analytics – Part I

Posted: July 9, 2015 in Uncategorized

Credit scoring represents the process of classifying or categorizing different individuals according to their credit worthiness. The term ‘credit’ originated from the Latin word ‘credo’, which means, ‘trust in’, or ‘rely on’. Therefore, in these kinds of problems, the objective is to identify individuals who are reliable to the banks i.e. least probable to default or delay in payment of interest. Let us discuss the process and model of building credit risk and credit score in analytics.

Why Credit Scoring

Broadly speaking, the method of credit scoring can be used to address the following concerns:

  • Estimation of credit Worthiness (willingness and ability to repay) of ‘ANY’ Customer
  • Identification of potential credit risk (the potential financial impact of any real or perceived change in borrowers’ Creditworthiness)
  • Classification of prime and sub-prime lenders (Classification among good customers)
Credit Risk Modeling

Credit Risk Modeling

Factors that Potentially affect Credit Worthiness in Analytics

There can be many factors that potentially affect credit worthiness of the individuals or borrowers and these factors need to be considered while developing a credit scoring model in analytics. Some of those factors are listed below. This is indicative and not an exhaustive list.

  • Borrower’s Age
  • Borrower’s Gender
  • Borrower’s Educational Qualification
  • Borrower’s Job Type (e.g. Private, Govt, Professional such as Doctor, Lawyer etc.)
  • Number of Years in Current Job
  • Borrower’s Total Experience
  • Borrower’s Current Income
  • Borrower’s Family Details (e.g. Age, Qualifications, Whether Working, Income, Number of Children and their Age etc.)
  • Borrower’s Previous Credit History (such as Current Obligations, EMIs, Payment Default Cases etc.)
  • Borrower’s Health Conditions and Insurances
  • Borrower’s Total Worth (e.g. Savings and Assets)

Techniques for Credit Scoring

There are different data mining techniques that can be used for classification and identification of credit worthiness of borrowers. Some of those are –

  • Logistic Regression
  • Decision Tree
  • Bayesian Networks
  • Random Forests

Among the above methods, logistic regression is the most popular one. Other methods are also used and known for their classification power.

Like any predictive modeling, in credit scoring, sampling plays a very important role. The possible sampling techniques are as follows:

  1. Random Sub-Sampling
  2. K-fold Cross Validation
  3. Leave-One-Out
  4. Bootstrap (Random Sampling With Replacement)

In the next part, we will discuss variable selection and model estimation for credit Scoring.


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