Show simple item record

dc.contributor.authorAkter, Sima
dc.date.accessioned2018-11-13T10:40:50Z
dc.date.available2018-11-13T10:40:50Z
dc.date.issued2018-09-08
dc.identifier.urihttp://hdl.handle.net/20.500.11948/3452
dc.description.abstractEvery lender’s organization such as banks and credit card companies use credit score system to determining the creditworthiness of their clients. Currently, they are using numerical scoring system in where the score determined by the compering new customer vs. existing customer profile. This does not capture the exact behavior of certain individual entities or more optimal ways to segment scoring models for which few loan trends to classify in a result organization are deprive of profit and lead to the loss. Now it analyzed that the problem can be optimized using Machine Learning technique and possible to forecast the behavior of the customer. In this study, we applied various machine learning technique to predict the classified loans, minimize credit risk and maximize the profit of the lender’s organization. Hence, this study intended to find the best modeling with best performance and accuracy by the comparing their results.en_US
dc.language.isoen_USen_US
dc.publisherDaffodil International Universityen_US
dc.subjectMachine learningen_US
dc.subjectCredit risk modeling systemen_US
dc.titleStudy on credit risk modeling system using Machine learning techniquesen_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record