{"id":633,"date":"2013-09-21T14:14:19","date_gmt":"2013-09-21T08:44:19","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=633"},"modified":"2018-02-17T12:40:15","modified_gmt":"2018-02-17T07:10:15","slug":"data-visualization-case-study-banking","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/data-visualization-case-study-banking\/","title":{"rendered":"Data Visualization – Banking Case Study Example (Part 1)"},"content":{"rendered":"
\"Leonardo<\/a>

Leonardo – by Roopam<\/p><\/div>\n

\n

A Scientist & An Artist<\/span><\/h2>\n

A few weeks ago while wandering around in Florence, the birthplace of the Renaissance, I could not escape the thought of Leonardo da Vinci : the greatest polymath of all times. Leonardo\u2019s illustrious resume contains titles such as painter, inventor, physicist, astronomer, engineer, biologist, anatomist, geologist, and architect \u2013 no kidding! A smart cat would have to live all her nine lives to acquire the nine titles Leonardo had mastered in one lifetime. Today, while discussing facets of data visualization, we should pay homage to Uncle Leonardo as we cross the realm of both art and science.<\/p>\n

Art and Science of Data Visualization<\/span><\/h2>\n
\"Data<\/a>

Data Visualization – by Roopam<\/p><\/div>\n

Data visualization, as mentioned earlier, is both art and science. I personally prefer to have a long look at the data, plotting them in various ways before jumping into rigorous mathematical modeling. You might have noticed my penchant for art while going through my artwork presented in all the posts on this blog. The saying \u2013 a picture is worth thousand words \u2013 holds true during data analysis as well. Models in analytics can go horribly wrong if you have not spent enough time on the data exploratory phase \u2013 which is all about data visualization to me. Let me present a case study example to explain the aspects of data visualization during the exploratory phase.<\/p>\n

Banking\u00a0Case Study Example – Risk Management<\/span><\/h2>\n

Assume you are the chief risk officer (CRO) for CyndiCat bank that has disbursed 60816 auto loans in the quarter between April\u2013June 2012. Today, about a year and a quarter since the loans disbursal, you know that the loans have seasoned or bad loans\u00a0are tagged to a greater certainty (read a detailed discussion)<\/a>. You have noticed a bad rate of around 2.5% or 1524 bad loans out of total 60816 disbursed loans.<\/p>\n

Before you jump to multivariate analysis and credit scoring (read a detailed discussion on credit scoring)<\/a>, you want to analyze the bad rate across several individual variables. You have a hunch based on your experience that borrower\u2019s age at the time of loan disbursal is a key distinguishing factor for bad rates. Therefore, you have divided the loans based on the age of the borrowers and created a table something like the one below.<\/p>\n

\"table<\/a><\/p>\n

Using the above table, you have created a histogram and zoomed into the area of interest (close to the bad loans) as shown in the plots below.<\/p>\n

\"Graph<\/a><\/p>\n

You must have noticed the following<\/p>\n

\u2022 The distribution of loans across age groups is a reasonably smooth normally distributed curve, without too many outliers. Age often display this kind of pattern for most products. However, do not expect similar smooth curves for other commonly found variables in a business scenario. Often, you may have to resolve to variable transformation to make the distributions smooth.<\/p>\n

\u2022 The maximum bad loans are in the age bucket 42 to 45 years. This certainly does not mean the risk is also the highest in this bucket, however, once I have heard someone drawing a similar conclusion in a quarterly business review meeting \u2013a silly mistake. Note, the maximum loans are also in the bucket 42 to 45 years. Absolute numbers do not provide enough information hence we need to create a normalized plot.<\/p>\n

\u2022 The data is really thin on the fringe buckets (i.e. <21 and >60 years groups)\u00a0with only 9 and 6 data points \u2013 be careful when dealing with such thin data. Sound business knowledge to modify these fringe buckets is extremely helpful while a model development. For instance, you know that for age above 60 for loans could be highly risky, but in this data, we do not have enough evidence for the same since we do not have enough data to validate our hypothesis. We should supplement a right risk weight in such situation \u2013 however, be very careful while doing so.<\/p>\n

Normalized Plot<\/span><\/h2>\n

The normalized plot is easy to construct. The idea is to scale each age group to 100% and overlay bad and good percentage of records on top. We could extend the table shown above to get the values for the normalized plot as shown below.<\/p>\n

\"table<\/a><\/p>\n

Now, once you have the table ready you could create a normalized plot quite easily, as shown below (again we have zoomed into the plot to get a clear view of bad rates).<\/p>\n

\"Normalized<\/a><\/p>\n

These plots are completely different from the original frequency count plot and presenting the information in a completely different light. The following are the things one could conclude from the plots.<\/p>\n

\u2022 There is a definite trend in terms of the bad rates and the age groups. As the borrowers are getting older, they are less likely to default on their loans. That is a good insight.<\/p>\n

\u2022 Again, the fringes (i.e. <21 and >60 years groups) have thin data, this information cannot be obtained from the normalized plot. Hence, you need to have the frequency plot handy to treat thin data differently. A handy rule of thumb is to have at least 10 records of both (good & bad) cases before taking the information seriously \u2013 otherwise, it is not statistically significant.<\/p>\n

I must conclude by saying that, data visualization is the beginning of modeling process and not the destination. However, it is a good & creative beginning.<\/p>\n

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

With big data, data analysis tools & technologies, scientific progress and democratic environment \u2013 we could be living in the Renaissance of our times. However, we will need more Leonardo da Vincis to make these times really special.<\/p>\n","protected":false},"excerpt":{"rendered":"

A Scientist & An Artist A few weeks ago while wandering around in Florence, the birthplace of the Renaissance, I could not escape the thought of Leonardo da Vinci : the greatest polymath of all times. Leonardo\u2019s illustrious resume contains titles such as painter, inventor, physicist, astronomer, engineer, biologist, anatomist, geologist, and architect \u2013 no<\/p>\n

Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":646,"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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[57,54],"tags":[8,7,6,10],"jetpack_publicize_connections":[],"yoast_head":"\nData Visualization - Banking Case Study Example (Part 1) – YOU CANalytics |<\/title>\n<meta name=\"description\" content=\"This is a case study example to estimate credit risk through data visualization. The entire case study example is presented in 6 parts.\" \/>\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\/data-visualization-case-study-banking\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Visualization - Banking Case Study Example (Part 1) – YOU CANalytics |\" \/>\n<meta property=\"og:description\" content=\"This is a case study example to estimate credit risk through data visualization. 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In our last article, we learned the procedure to visualize risk across a parameter (age groups) in Excel. In this part we will generate the same visualization in R. This lab exercise is a part of the banking\u2026","rel":"","context":"In "Analytics Labs"","block_context":{"text":"Analytics Labs","link":"https:\/\/ucanalytics.com\/blogs\/category\/analytics-labs\/"},"img":{"alt_text":"Presentation3","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/05\/Presentation3.jpg?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/05\/Presentation3.jpg?resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/05\/Presentation3.jpg?resize=525%2C300 1.5x"},"classes":[]},{"id":2912,"url":"https:\/\/ucanalytics.com\/blogs\/excel-use-secondary-axis-to-create-two-y-axes-data-visualization-banking-case-lab\/","url_meta":{"origin":633,"position":1},"title":"Data Visualization – Banking Case Lab : Microsoft Excel – use Secondary Axis to Create Two Y Axes","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Analytics Lab Malcolm Gladwell in his book 'Outliers' says that it takes approximately 10,000 hours of training or practice for anybody to master a subject. There is nothing in the world that could beat hands-on practice for learning. Keeping this in mind let me start a new series on YOU\u2026","rel":"","context":"In "Analytics Labs"","block_context":{"text":"Analytics Labs","link":"https:\/\/ucanalytics.com\/blogs\/category\/analytics-labs\/"},"img":{"alt_text":"Presentation1","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/05\/Presentation1.jpg?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/05\/Presentation1.jpg?resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/05\/Presentation1.jpg?resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/05\/Presentation1.jpg?resize=700%2C400 2x"},"classes":[]},{"id":695,"url":"https:\/\/ucanalytics.com\/blogs\/data-visualization-case-study-banking-part-2\/","url_meta":{"origin":633,"position":2},"title":"Data Visualization – Banking Case Study Example (Part 2)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Sherlock Holmes & Data Visualization As a kid, a friend of mine used to own a Sherlock Holmes toy kit \u2013 the source of envy for all the other friends. The kit had a Sherlock Holmes cap, a pipe, a watch and a magnifying glass. The magnifying glass was the\u2026","rel":"","context":"In "Banking Risk Case Study Example"","block_context":{"text":"Banking Risk Case Study Example","link":"https:\/\/ucanalytics.com\/blogs\/category\/risk-analytics\/banking-risk-case-study-example\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/09\/Sherlock-Holmes-Copy.jpg?fit=459%2C458&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]},{"id":11933,"url":"https:\/\/ucanalytics.com\/blogs\/how-data-science-will-shape-post-covid-banking-video-discussion\/","url_meta":{"origin":633,"position":3},"title":"How data science will shape post-COVID banking? – Video Discussion","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"How data science will shape post-COVID banking? had a thought-provoking discussion with\u00a0FrankBanker: https:\/\/www.youtube.com\/watch?v=o-3uPgjSFSY&feature=youtu.be 02:01 (Part 1) Impact on variables in Credit Models05:33 (Part 2) Are we going back to Judgemental Lending?07:50 (Part 3) Evaluating analytics readiness of Banks12:20 (Part 4) Is \u2018IT\u2019 the right place for Data Analytics?14:15 (Part 5)\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2020\/07\/Data-Analytics-for-Banking-FB.jpg?fit=788%2C444&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2020\/07\/Data-Analytics-for-Banking-FB.jpg?fit=788%2C444&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2020\/07\/Data-Analytics-for-Banking-FB.jpg?fit=788%2C444&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2020\/07\/Data-Analytics-for-Banking-FB.jpg?fit=788%2C444&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":886,"url":"https:\/\/ucanalytics.com\/blogs\/information-value-and-weight-of-evidencebanking-case\/","url_meta":{"origin":633,"position":4},"title":"Information Value (IV) and Weight of Evidence (WOE) \u2013 A Case Study from Banking (Part 4)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"This is a continuation of our banking case study for scorecards development. 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Data science\u00a0models in some sense are the precursor to artificial intelligence. 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