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Credit Scorecards – Variables Selection (part 3 of 7)

· Roopam Upadhyay 8 Comments

Variables Selection in Predictive Analytics

Predictive Analytics: Variables Selection - by Roopam Upadhyay

Predictive Analytics: Variables Selection – by Roopam

The following story goes back to the time when I just started my transition from physics to business. I met this investment banker* in his mid-thirties during a Friday night party. After gulping down a few pints of beer, his mood became a bit somber and he told me how he hates his job. However, he had a plan of working his ass off until he retires at 45. Then he will do everything that makes him happy. I was thoroughly confused, how could someone debar himself from an emotion – happiness – for so many years and rediscover it later? I was wondering about the recipe for happiness – raindrops on roses and whiskers on kittens. An individual’s happiness is a tricky thing; however, I shall attempt to tackle this issue in my later article on logistic regression. For now, let us try to explore how states measure the collective well-being of their people. I shall use this topic of population well-being to explore an interesting topic in analytical scorecard development: variables selection.

Variables Selection – Lessons from GDP & GNH

The most popular measure for national prosperity, unanimously projected by economists and TV channels, is Gross Domestic Product (GDP). The equation for measuring GDP as taught in macroeconomics 101 is:

GDP Equation

Clearly, there are 5 factors/variables that govern GDP according to this equation. The first look at GDP as a measure for national well-being seemed incomplete to me. All the variables for GDP were from commerce. They are important but cannot be the only factors for country’s well-being, more so in a highly diverse & complicated country like India.

Gross National Happiness Index – The Story of Bhutan Naresh

Variables Selection – by Roopam

Ok, so what else do we have? A lesser-known index is Gross National Happiness (GNH). The origins of GNH are in Bhutan. They measure their country’s progress through GNH. The term was coined and implemented by Jigme Singye Wangchuck. This name immediately takes me back to the early nineties live telecast of the SAARC summit by India’s national broadcaster Doordarshan (DD). The old-timer Hindi commentators were referring to a modest man in a bathrobe-like-attire as ‘Bhutan Naresh’ – King of Bhutan. At first glance, he did not fit well with the power horses of the south Asian region. Nevertheless, he seems to have devised a more holistic metric to measure his country’s well-being. GNH is a combination of the following broad categories:

1. Living standard & income
2. Health coverage
3. Physiological well-being
4. Time spent at work and relaxing
5. Good governance
6. Schooling & education
7. Cultural diversity
8. Community vitality
9. Environmentalism and conservatism

There are 72 total variables in GNH measured on a scale of 0 to 1, such as daily hours of sleep and trust in media; hmmm, not a bad start! You could do your own research on GNH and let me know what you feel about it. Actually, we can work out our own formula for a GNH like metric. The idea is to select the right variables to build your model!

Variables Selection in Credit Scoring

In data mining and statistical model building exercises, similar to credit scoring, variables selection process is performed through statistical significance – a reasonably automated process through advanced software. However, the variables are still created and measured by humans. High impact analyses in businesses are still driven by hunches. Human intelligence is not obsolete yet.

In one of the projects I did with a financial organization, the result of credit risk analysis and scoring led to redesigning of the application form. Application forms are a major source of data collection regarding the borrower. However, nobody wants to fill a lengthy form hence an optimal size of the form ensures accurate information provided by the borrower. The idea is to select the right variable and ensure accurate measurement.

There are several aspects regarding variables but I will mention just one of them here (coarse classing).

Coarse Classing in Credit Scoring

3 Shoe measureOne of my favorite activities as a kid was going to a shoe store and getting my feet measured every summer before the school started. The shoe shops had a strange, miniature, slide-like device to measure foot size. It was fun to see my feet grow from one size to another every year or two. The growth was quantized i.e you are size-2 or 3 never 2.5 or 2.7. This aspect of converting measure such as 2.5 & 2.7 to 3 is called grouping, bucketing or classing. This is an integral part of creating scorecards that you will find in all the books I have listed in the first part of this blog series.

I have been a part of several heated discussions on the relevance of coarse class in scorecard development throughout my career. In most, if not all academic articles you will rarely see coarse classing as a technique during model development. Quite a few academicians & practitioners for a good reason believe that coarse classing results in loss of information. However, in my opinion, coarse classing has the following advantage over using raw measurement for a variable.

1. It reduces random noise that exists in raw variables – similar to averaging and yes, you lose some information here.
2. It handles extreme events – on two extremes of a variable – much better where you have thin data.
3. It handles the non-linear relationship between dependent and independent variable without a lot of effort of variable transformation from the analyst.

Sign-off Note 

We are half way through this series on ‘Analytical Scorecard Development’ and I am enjoying writing this thoroughly. I hope as a reader you are on the same page. Scorecard building is highly technical and I have tried to discuss some aspects with easy to understand examples. However, to manage the length of the article, I am not able to get into the details. I must say that I love the details! So, if you have any queries, doubts, points-of-view or recommendations please write back on the discussion board or on my email: roopam.up@gmail.com

*I do not remember correctly if he was an investment banker but it fits the description better – I should also not refrain from treating the community like a punching bag (going by the popular emotions).
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Related

Posted in Credit Risk Analytics Series, Risk Analytics | Tags: Banking and Insurance Analytics, Business Analytics, Credit Risk, Predictive Analytics, Roopam Upadhyay |
« Credit Scorecards – Classification Problem (part 2 of 7)
Credit Scorecards – Advanced Analytics (part 4 of 7) »

8 thoughts on “Credit Scorecards – Variables Selection (part 3 of 7)”

  1. Joy says:
    May 10, 2015 at 8:16 pm

    Hi, Can You please also point to some other online resources that you might follow in terms of getting insights on risk analytics topics esp. credit risk. Thnx. in advance..

    Reply
    • Roopam Upadhyay says:
      May 14, 2015 at 9:38 am

      Hi, I suggest you read the following case study

      1) Banking case study

      Also, refer to the books described at the end of the following article
      2) Books for scorecard development

      Reply
  2. Vinayak says:
    May 20, 2015 at 1:08 pm

    Hi Roopam,

    It’s a very good article. I was thoroughly enjoying it. Hats off to your creative thinking and easy way of explanation.

    Thanks,
    Vinayak

    Reply
    • Roopam Upadhyay says:
      May 25, 2015 at 8:43 pm

      Thanks Vinayak

      Reply
  3. khyati says:
    March 27, 2016 at 5:09 pm

    Hi, your blogs are very insigthful and give detailed and sharp view on analytics. Could you please share more on Roll rate analysis and vintage analysis…?

    Reply
  4. Abhishikta Chakraborty says:
    May 1, 2017 at 9:20 am

    Hi Roopam,

    Your blog is very insightful.

    In your blog you have mentioned that, “There are several aspects regarding variables”. Could you please put on some light on what are all several aspects?

    For the detailed description of Coarse Classing, which book will be good?

    Thanks in advance.

    Thanks & Regards,
    Abhishikta

    Reply
  5. Yohann says:
    July 3, 2017 at 9:08 pm

    Hi ,

    Thank you for the articles you publish. They are truly clear and instructive.

    Reply
  6. June says:
    July 23, 2018 at 6:58 am

    Thanks for sharing such interesting story with experiences.
    You group “coarse classing” in to select variable, they seems of different leves.

    Reply

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