{"id":10206,"date":"2020-09-04T16:47:47","date_gmt":"2020-09-04T16:47:47","guid":{"rendered":"http:\/\/ismiletechnologies.com\/?p=3217"},"modified":"2021-10-11T18:53:13","modified_gmt":"2021-10-11T13:23:13","slug":"banking-credit-card-spend-prediction","status":"publish","type":"post","link":"https:\/\/ismiletechnologies.com\/en_us\/cloud-services\/banking-credit-card-spend-prediction\/","title":{"rendered":"Banking Credit Card Spend Prediction"},"content":{"rendered":"\t\t
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Idea and Concept:\u00a0<\/span><\/b>\u00a0<\/span><\/p>

Credit scores are designed to make decisions easier for lenders. The bank would like to understand what factors are driving credit card spend. The bank wants to use these insights to calculate credit limit. Credit scores help lenders decide whether or not to approve loan applications and determine what loan terms to offer. The scores are generated by algorithms using information from the credit reports, which summarize the borrowing history of a customer.\u00a0<\/span>\u00a0<\/span><\/p>

Objective and approach:\u00a0<\/span><\/b>\u00a0<\/span><\/p>

The objective of our project is to understand what\u2019s driving the total spend (Primary Card + Secondary card). Given the factors, predict credit limit for the new applicants. Dataset available was in xlsx format. The data have been provided for 5000 customers. Detailed data dictionary has been provided for understanding the data in the data. Data is encoded in the numerical format to reduce the size of the data however some of the variables are categorical. You can find the details in the data dictionary.\u00a0<\/span>\u00a0<\/span><\/p>

Project<\/span><\/b>\u202f<\/span><\/b>walk-through:\u00a0<\/span><\/b>\u00a0<\/span><\/p>

In general, the collected data was for 5000 customers and we wished to understand what is driving the total spends of credit card and prioritize the drivers based on the importance. The cloud platform used for the project is GCP. So, after acquiring the data, it was ingested to Google cloud storage and AI platform notebooks\u00a0<\/span>were<\/span>\u00a0used for the deployment of models. This process included intensive data cleaning or preparation, generating correlation matrix, performing factor analysis; filtering variables using correlation as well as Linear Regression\u00a0modelling. Finally, we built data visualizations on Power BI.\u00a0<\/span>\u00a0<\/span><\/p>

Conclusions:\u00a0<\/span><\/b>\u00a0<\/span><\/p>

Following are a few conclusions corresponding to the columns.\u00a0<\/span>\u00a0<\/span><\/p>

lncreddebt\u00a0:\u00a0log of credit card debt in thousands.\u00a0If user has more debt on credit\u00a0<\/span>card,\u00a0then his credit card limit is more and\u00a0the user\u00a0<\/span>is\u00a0likely to have more income. If user has more debt on credit card,\u00a0then she\/he spends more with credit card and haven\u2019t repaid.\u00a0<\/span>\u00a0<\/span><\/p>

carcatvalue: Primary vehicle price category.\u00a0Person owning economic car likely to have less credit card spend as she\/he would prefer more on saving and likely to defer from buying expensive items.\u00a0<\/span>\u00a0<\/span><\/p>

debtinc\u00a0:\u00a0If debt to income ratio is high,\u00a0person is likely to spend less.<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Idea and Concept:\u00a0\u00a0 Credit scores are designed to make decisions easier for lenders. The bank would like to understand what factors are driving credit card spend. The bank wants to use these insights to calculate credit limit. Credit scores help lenders decide whether or not to approve loan applications and determine what loan terms to […]<\/p>\n","protected":false},"author":3,"featured_media":5793,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[81],"tags":[82],"class_list":["post-10206","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cloud-services","tag-credit-card"],"_links":{"self":[{"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/posts\/10206","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/comments?post=10206"}],"version-history":[{"count":3,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/posts\/10206\/revisions"}],"predecessor-version":[{"id":18766,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/posts\/10206\/revisions\/18766"}],"wp:attachment":[{"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/media?parent=10206"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/categories?post=10206"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/tags?post=10206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}