Standard Bank Tech Impact Challenge: Xente Credit Scoring Challenge

Predict the likelihood of credit default of ecommerce clients
Xente is an e-commerce and financial service app serving 30,000+ customers in Uganda.
This dataset includes a sample of approximately 2,665 unique e-commerce transactions that occurred between 21 September 2018 and 17 July 2019. During this period, 1,631 loans were issued to Xente clients.
The data have been split into a test and training set. This was done chronologically, so the buyers' history can be used to predict their default likelihood.
The training set contains 1,769 unique transactions and the test set contains 905 unique transactions. The number of observations in the train data sets exceeds the number of transactions, as a result of some transactions being paid in split payments/installments.
Variable definitions
CustomerId: Unique number identifying the customer on platform
TransactionStartTime: Transaction start time
Value: Value of transaction
Amount: Value of Transaction with charges
TransactionId: Unique transaction identifier on platform
BatchId: Identifier for bulk transactions being done on an account
SubscriptionId: You can have one account with multiple subscriptions
CurrencyCode: Country currency
CountryCode: Numerical geographical code of country
ProviderId: Source provider of Item bought
ProductId: Item name being bought
ProductCategory: Type of product
ChannelId: Identifies if customer used Xente Paylater on any other channel
TransactionStatus: Loan application status (1=accepted, 0 = rejected)
IssuedDateLoan: Date loan is issued
AmountLoan: Value of the loan issued
Currency: Ugandan shillings Denominations
LoanId: Loan transaction unique identifier
PaidOnDate: Date on which the loan was paid
IsFinalPayBack: Last payback installment
InvestorId: Loan issuer or network owner
DueDate: Date loan is due
LoanApplicationId: unique identifier for loan application
PayBackId: Loan payback number identifier
ThirdPartyId: Transaction id for a loan payback
IsThirdPartyConfirmed: loan order succeeded on platform
IsDefaulted: Exceeded agreed payback time (1 = default, 0 = non-default)
Files available for download
The files for download here are:
VariableDefinitions.csv: Definition of the features per transaction
Train.csv: Ecommerce transactions and associated loans from 21 September 2018 and 31 March 2019, including whether or not a customer defaulted on their loan. This is the dataset that you will use to train your model.
Test.csv: Ecommerce transactions and associated loans from 31 March 2019 and 17 July 2019, excluding whether or not a customer defaulted on their loan. Additional variables associated with the loan have also been removed from the test set. is the dataset that you will use to test your model on.
unlinked_masked_final.csv: e-commerce transactions not linked to any loans, but associated to customers with loan-linked e-commerce transactions.
sample_submission.csv: is an example of what your submission file should look like, including a list of unique transaction ids and the associated loan status. The order of the rows does not matter, but the names of the TransactionId must be correct.