2 datasets found
  1. L&T Vehicle Loan Default Prediction

    • kaggle.com
    zip
    Updated Apr 23, 2019
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    Gaurav (2019). L&T Vehicle Loan Default Prediction [Dataset]. https://www.kaggle.com/gauravdesurkar/lt-vehicle-loan-default-prediction
    Explore at:
    zip(12451853 bytes)Available download formats
    Dataset updated
    Apr 23, 2019
    Authors
    Gaurav
    Description

    Context

    Financial institutions incur significant losses due to the default of vehicle loans. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by these institutions. This warrants a study to estimate the determinants of vehicle loan default. A financial institution has hired you to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date. Following Information regarding the loan and loanee are provided in the datasets: Loanee Information (Demographic data like age, Identity proof etc.) Loan Information (Disbursal details, loan to value ratio etc.) Bureau data & history (Bureau score, number of active accounts, the status of other loans, credit history etc.) Doing so will ensure that clients capable of repayment are not rejected and important determinants can be identified which can be further used for minimising the default rates.

  2. L&T Vehicle Loan Default Prediction

    • kaggle.com
    zip
    Updated Apr 18, 2019
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    Mamta Dhaker (2019). L&T Vehicle Loan Default Prediction [Dataset]. https://www.kaggle.com/mamtadhaker/lt-vehicle-loan-default-prediction
    Explore at:
    zip(12085164 bytes)Available download formats
    Dataset updated
    Apr 18, 2019
    Authors
    Mamta Dhaker
    Description

    Financial institutions incur significant losses due to the default of vehicle loans. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by these institutions. This warrants a study to estimate the determinants of vehicle loan default. A financial institution has hired you to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date. Following Information regarding the loan and loanee are provided in the datasets: Loanee Information (Demographic data like age, Identity proof etc.) Loan Information (Disbursal details, loan to value ratio etc.) Bureau data & history (Bureau score, number of active accounts, the status of other loans, credit history etc.) Doing so will ensure that clients capable of repayment are not rejected and important determinants can be identified which can be further used for minimising the default rates.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Gaurav (2019). L&T Vehicle Loan Default Prediction [Dataset]. https://www.kaggle.com/gauravdesurkar/lt-vehicle-loan-default-prediction
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L&T Vehicle Loan Default Prediction

Vehicle Loan Default Prediction

Explore at:
zip(12451853 bytes)Available download formats
Dataset updated
Apr 23, 2019
Authors
Gaurav
Description

Context

Financial institutions incur significant losses due to the default of vehicle loans. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by these institutions. This warrants a study to estimate the determinants of vehicle loan default. A financial institution has hired you to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date. Following Information regarding the loan and loanee are provided in the datasets: Loanee Information (Demographic data like age, Identity proof etc.) Loan Information (Disbursal details, loan to value ratio etc.) Bureau data & history (Bureau score, number of active accounts, the status of other loans, credit history etc.) Doing so will ensure that clients capable of repayment are not rejected and important determinants can be identified which can be further used for minimising the default rates.

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