Felix-ai / Xente_Credit_Scoring_Challenge

Predict from transactions and repayments the likelihood of default in each transaction.

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Standard Bank Tech Impact Challenge: Xente credit scoring challenge

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.

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Predict from transactions and repayments the likelihood of default in each transaction.