{"id":8,"date":"2013-07-01T17:01:32","date_gmt":"2013-07-01T11:31:32","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=8"},"modified":"2017-04-30T10:41:48","modified_gmt":"2017-04-30T05:11:48","slug":"credit-scorecards-part-1","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/credit-scorecards-part-1\/","title":{"rendered":"Credit Scorecards – Introduction (part 1 of 7)"},"content":{"rendered":"
\n

Credit Scorecards in the Age of Credit Crisis<\/span><\/h2>\n

This incident took place at a friend\u2019s 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 \u2013 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 \u2013 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.<\/p>\n

\"Predictive<\/a>

Predictive Analytics: The lurking Danger – by Roopam<\/p><\/div>\n

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 \u2013 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.<\/strong><\/p>\n

Scorecards and Predictive Analytics<\/span><\/h2>\n

In the following series, we will explore the practitioners\u2019 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\u00a0present an extremely broad methodology\/thought-process that has multiple business applications. A few applications for classification problem are:<\/p>\n

\u2022 Application or credit scorecards to assess repayment risk of the borrower<\/div>\n
\u2022 Image analytics of MRI to identify if the cancer is benevolent or malignant
\n\u2022 Behavioral models to identify the most probable future action of the customer<\/div>\n
\u2022 Identification of potential drug targets in the protein structure
\n\u2022 Fraud detection models<\/div>\n
\u2022 Sentiment analysis of Tweets and Facebook posts
\n\u2022 Cross\/up sell propensity models
\n\u2022 Campaign response models
\n\u2022 Insurance ratings<\/div>\n

 
\nFor 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.<\/p>\n

\"1<\/a>

Credit Scoring: Development Stages of Credit Scorecard – by Roopam<\/p><\/div>\n

Flow of Subsequent Articles<\/span><\/h4>\n

The flow of subsequent articles in the series will be as following<\/p>\n

1. Classification problem and sampling
\n2. Variable selection and coarse classing
\n3. Predictive Models
\n4. Logistic regression and scorecards
\n5. Model validation
\n6. Application and business process integration<\/p>\n

Books for Credit Scorecards\u00a0<\/span><\/h4>\n

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\u2019s 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.<\/p>\n

1. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring – Naeem Siddiqi
\n2. Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior – Steven Finlay
\n3. Credit Scoring for Risk Managers: The Handbook for Lenders – Elizabeth Mays and Niall Lynas
\n4. The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation – Raymond Anderson
\n5. Credit Risk Models \u2013 Elizabeth Mays<\/p>\n

Sign-off Note<\/span><\/h4>\n

Look forward to sharing my views on predictive analytics and hearing back from you. See you soon with the second part of this series.<\/p>\n","protected":false},"excerpt":{"rendered":"

Credit Scorecards in the Age of Credit Crisis This incident took place at a friend\u2019s 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 \u2013<\/p>\n

Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":7237,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[55,54],"tags":[8,7,69,6,10],"jetpack_publicize_connections":[],"yoast_head":"\nCredit Scorecards : Introduction - YOU CANalytics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ucanalytics.com\/blogs\/credit-scorecards-part-1\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Credit Scorecards : Introduction - YOU CANalytics\" \/>\n<meta property=\"og:description\" content=\"Credit Scorecards in the Age of Credit Crisis This incident took place at a friend\u2019s party circa 2009, in the backdrop of the worst financial crisis the planet has seen for a long time. 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At seven, I adored him because his mistakes were applauded and became part of history \u2013 Columbus mistook Native Americans for Indians because he thought he had\u2026","rel":"","context":"In "Credit Risk Analytics Series"","block_context":{"text":"Credit Risk Analytics Series","link":"https:\/\/ucanalytics.com\/blogs\/category\/risk-analytics\/credit-risk-analytics-series\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/07\/7-Leader-225x300.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":110,"url":"https:\/\/ucanalytics.com\/blogs\/credit-scorecards-logistic-regression-part-5\/","url_meta":{"origin":8,"position":4},"title":"Credit Scorecards – Logistic Regression (part 5 of 7)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"A Primer on Logistic Regression - Are you Happy? A few years ago, my wife and I took a couple of weeks\u2019 vacation to England and Scotland. Just before boarding the British Airway\u2019s plane, an air-hostess informed us that we were upgraded to business class. Jolly good! What a wonderful\u2026","rel":"","context":"In "Credit Risk Analytics Series"","block_context":{"text":"Credit Risk Analytics Series","link":"https:\/\/ucanalytics.com\/blogs\/category\/risk-analytics\/credit-risk-analytics-series\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/07\/5-Logit-Happy.jpg?fit=614%2C386&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/07\/5-Logit-Happy.jpg?fit=614%2C386&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/07\/5-Logit-Happy.jpg?fit=614%2C386&ssl=1&resize=525%2C300 1.5x"},"classes":[]},{"id":230,"url":"https:\/\/ucanalytics.com\/blogs\/credit-scorecards-model-validation-part-6\/","url_meta":{"origin":8,"position":5},"title":"Credit Scorecards – Model Validation (Part 6 of 7)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"There are only two ways to live your life. One is as though nothing is a miracle. The other is as though everything is a miracle. \u2013 Albert Einstein A Commentary on Curiosity\u00a0 I think the best way to appreciate and enjoy the trivial is to travel. When I say\u2026","rel":"","context":"In "Credit Risk Analytics Series"","block_context":{"text":"Credit Risk Analytics Series","link":"https:\/\/ucanalytics.com\/blogs\/category\/risk-analytics\/credit-risk-analytics-series\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/07\/6-Travel-272x300.jpg?resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/8"}],"collection":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/comments?post=8"}],"version-history":[{"count":0,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/8\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media\/7237"}],"wp:attachment":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media?parent=8"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/categories?post=8"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/tags?post=8"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}