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Credit Scorecards – Introduction (part 1 of 7)

· Roopam Upadhyay 12 Comments

Credit Scorecards in the Age of Credit Crisis

This incident took place at a friend’s party circa 2009, in the backdrop of the worst financial crisis the planet has seen for a long time. The average Joe on the street was aware of terms such as mortgaged-backed securities (MBS), sub-prime lending and credit crisis – the reasons for his plight. Back to our party, I met an informed & compassionate elderly woman and after a few minutes of chitchat, the topic came to what I do for a living. At that point, I was working on a project of developing credit-scorecard for a leading mortgage lender in Mumbai. As I started explaining the details of my job, her expression changed from curious to angst and pain. Eventually, she interrupted and said – why would you do such a thing? Is this not the reason for all the mess? I was used to this reaction and had to correct her misconception.

Predictive Analytics: The lurking Danger - by Roopam

Predictive Analytics: The lurking Danger – by Roopam

Credit or application scorecards can be excellent tools for both lender and borrower to work out debt serving capability of the borrower. For lenders, scorecards can help them assess the creditworthiness of the borrower and maintain a healthy portfolio – which will eventually influence the economy as a whole. Additionally to the borrower, they can provide valuable information such as 45% of people with her socio-economic background have struggled to keep up with the EMI commitment. This could help the borrower make a well-informed decision before getting into a debt trap. Blaming science for reckless human behavior is not new. I believe, any rigorous science with practical applications is like a sharp German blade, a master chef prepares delicious meals with it and the irresponsible leaves a deep and painful cut.

Scorecards and Predictive Analytics

In the following series, we will explore the practitioners’ approach for developing and maintaining a scorecard. At a very high-level, credit scorecards have their roots in the classification problem in statistics & data mining. The classification problems present an extremely broad methodology/thought-process that has multiple business applications. A few applications for classification problem are:

• Application or credit scorecards to assess repayment risk of the borrower
• Image analytics of MRI to identify if the cancer is benevolent or malignant
• Behavioral models to identify the most probable future action of the customer
• Identification of potential drug targets in the protein structure
• Fraud detection models
• Sentiment analysis of Tweets and Facebook posts
• Cross/up sell propensity models
• Campaign response models
• Insurance ratings

 
For that matter, there are subtle links between credit scorecards and other models mentioned above. The details of these models could be drastically different but the underlining idea for these models is linked to the classification problem. In this series, I shall focus on credit or application scorecard methodology but will try to bring in other another scorecards and models whenever possible.

1 Credit Scoring Schematic

Credit Scoring: Development Stages of Credit Scorecard – by Roopam

Flow of Subsequent Articles

The flow of subsequent articles in the series will be as following

1. Classification problem and sampling
2. Variable selection and coarse classing
3. Predictive Models
4. Logistic regression and scorecards
5. Model validation
6. Application and business process integration

Books for Credit Scorecards 

I have compiled a list of books you may find useful while learning about analytical scorecards. The first four of these books have more or less the same flow, with Anderson’s book (#4) a little more detailed. However, you could choose any one of these four books without losing much .The last book (#5) is a collection of articles / papers by practitioners and academicians and is quite interesting.

1. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring – Naeem Siddiqi
2. Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior – Steven Finlay
3. Credit Scoring for Risk Managers: The Handbook for Lenders – Elizabeth Mays and Niall Lynas
4. The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation – Raymond Anderson
5. Credit Risk Models – Elizabeth Mays

Sign-off Note

Look forward to sharing my views on predictive analytics and hearing back from you. See you soon with the second part of this series.

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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) »

12 thoughts on “Credit Scorecards – Introduction (part 1 of 7)”

  1. Dave says:
    September 5, 2013 at 4:54 pm

    Excellent posts, I find all the parts quite helpful.

    Reply
  2. Deepak Kala says:
    June 8, 2014 at 7:07 pm

    Thanks for an awesome article on cerdit scorecard Roopam. I am new to the field of analytics and am finding your articles of gr8 help for an insight into this world.
    Please keep up with the good work and a beacon of light for people who desire to start a career in analytics.

    Reply
    • Roopam Upadhyay says:
      June 11, 2014 at 8:26 pm

      Thanks Deepak!

      Reply
  3. Harish says:
    September 26, 2014 at 6:01 pm

    Roopam – just one word which can describe your explanation of Analytics ” Awesome”. The examples that you used in the beginning of article capture interest and entice everyone to read the entire article. Its more like a fictional novel, that you want to finish before taking any other task.

    Reply
  4. Rajat says:
    November 23, 2015 at 11:22 pm

    Excellent post. Your way of explaining is simple yet powerful. I Request you to suggest some book or case study on fraud detection model.

    Reply
    • Roopam Upadhyay says:
      April 26, 2016 at 4:14 pm

      Personally, I have learned fraud analytics while working with banks and financial institutions and have never used a book for it. But this one looks interesting to me :
      Data Mining for Intelligence, Fraud & Criminal Detection: Advanced Analytics & Information Sharing Technologies by Christopher Westphal

      Reply
  5. Reva Maheshwari says:
    April 25, 2016 at 7:34 pm

    Hi Roopam,

    As the article states that this approach is helpful in development of different applications in both financial and non-financial industry. A few common examples are:

    • Behavioural Scorecards
    • Fraud detection models
    • Cross/up sell propensity models
    • Campaign Response models
    • Insurance ratings

    Can you please provide list of books that one can read to build knowledge in other areas as well while waiting for your articles :)?

    As always tons of gratitude for good work!

    Reply
    • Roopam Upadhyay says:
      April 26, 2016 at 4:09 pm

      Let me suggest a few books that you will find useful:

      Several of these R & Python books have case study examples in marketing and risk management. I suggest you start with these books.

      – R Books
      – Python Books

      For marketing analytics you might find these books useful:
      – Mastering Data Mining: The Art and Science of Customer Relationship Management by Michael J. A. Berry
      – Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman
      – Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python – by Thomas W. Miller

      Personally, I have learned fraud analytics while working with banks and financial institutions and have never used a book for it. But this one looks interesting to me :

      Data Mining for Intelligence, Fraud & Criminal Detection: Advanced Analytics & Information Sharing Technologies by Christopher Westphal

      Reply
      • Reva Maheshwari says:
        May 2, 2016 at 11:12 am

        Thank you Roopam. This information is of immense help!

        Reply
  6. Narasimhan KV says:
    April 11, 2018 at 2:15 pm

    Hello Roopam,

    Thank you for the great article,However i did not get the concept of a rolling performance window?

    Thanks

    Narasimhan

    Reply
  7. KG says:
    October 25, 2018 at 1:07 am

    Hey Roopam!

    You are doing a great service to self-learners and innovators who will benefit from your analytics. Can you suggest a good source of data paid/unpaid for this study to make into a real life project?

    Reply
  8. Rupesh Kumar says:
    May 8, 2020 at 1:22 pm

    Hi Roopam,

    Can we get these books as free download which you mentioned above ?

    Thanks,
    Rupesh Kumar

    Reply

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