README.md

---
license: cc-by-4.0
language:
- ru
tags:
- bank
- loan
- time-series
size_categories:
- 1M<n<10M
pretty_name: Alfa BKI
---

### Dataset Summary

Alfa BKI is a unique high-quality dataset collected from the real data source of credit history bureaus (in Russian "бюро кредитных историй/БКИ"). It contains the history of corresponding credit products and the applicants' default on the loan.

### Supported Tasks and Leaderboards

The dataset is supposed to be used for training models for the classical bank task of predicting the default of the applicant.    

## Dataset Structure

### Data Instances

The example of one sample is provided below
```
{
  'app_id': 0,
  'history':
  [
    [ 0, 1, 18, 9, 2, 3, 16, 10, 11, 3, 3, 0, 2, 11, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 16, 2, 17, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 1, 3, 4, 1, 0, 0 ],
    [ 0, 2, 18, 9, 14, 14, 12, 12, 0, 3, 3, 0, 2, 11, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 16, 2, 17, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 1, 3, 4, 1, 0, 0 ],
    [ 0, 3, 18, 9, 4, 8, 1, 11, 11, 0, 5, 0, 2, 8, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 15, 2, 17, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 4, 1, 2, 3, 1, 1, 1 ],
    [ 0, 4, 4, 1, 9, 12, 16, 7, 12, 2, 3, 0, 2, 4, 6, 16, 5, 4, 8, 0, 1, 1, 1, 1, 16, 2, 17, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 1, 3, 1, 1, 0, 0 ],
    [ 0, 5, 5, 12, 15, 2, 11, 12, 10, 2, 3, 0, 2, 4, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 16, 2, 17, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 1, 3, 4, 1, 0, 0 ],
    [ 0, 6, 5, 0, 11, 8, 12, 11, 4, 2, 3, 0, 2, 4, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 9, 5, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 4, 3, 3, 3, 4, 1, 2, 3, 1, 0, 1 ],
    [ 0, 7, 3, 9, 1, 2, 12, 14, 15, 5, 3, 0, 2, 3, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 16, 2, 17, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 1, 3, 4, 1, 0, 0 ],
    [ 0, 8, 2, 9, 2, 3, 12, 14, 15, 5, 3, 0, 2, 13, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 16, 2, 17, 1, 1, 1, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 1, 3, 4, 1, 0, 0 ],
    [ 0, 9, 1, 9, 11, 13, 14, 8, 2, 5, 1, 0, 2, 11, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 1, 2, 17, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 1, 2, 4, 1, 0, 0 ],
    [ 0, 10, 7, 9, 2, 10, 8, 8, 16, 4, 2, 0, 2, 11, 6, 16, 5, 4, 8, 1, 1, 1, 1, 1, 15, 2, 17, 0, 1, 1, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 4, 1, 2, 4, 1, 0, 0 ]
  ],
  'flag': 0
}
```

### Data Fields

- `id`: application ID.
- `history`: an array of transactions where each credit product is represented as a 37-dimensional array, each element of the array represents a corresponding feature from the following list.
  - `id`: application ID.
  - `rn`: serial number of the credit product in the credit history.
  - `pre_since_opened`: days from the date of opening the loan to the date of data collection.
  - `pre_since_confirmed`: days from the date of confirmation of the loan information to the date of data collection.
  - `pre_pterm`: planned number of days from the opening date of the loan to the closing date.
  - `pre_fterm`: actual number of days from the opening date of the loan to the closing date.
  - `pre_till_pclose`: planned number of days from the date of data collection to the closing date of the loan.
  - `pre_till_fclose`: actual number of days from the date of data collection to the closing date of the loan.
  - `pre_loans_credit_limit`: credit limit.
  - `pre_loans_next_pay_summ`: amount of the next loan payment.
  - `pre_loans_outstanding`: remaining unpaid loan amount.
  - `pre_loans_total_overdue`: current overdue debt.
  - `pre_loans_max_overdue_sum`: maximum overdue debt.
  - `pre_loans_credit_cost_rate`: full cost of the loan.
  - `pre_loans5`: number of delays up to 5 days.
  - `pre_loans530`: number of delays from 5 to 30 days.
  - `pre_loans3060`: number of delays from 30 to 60 days.
  - `pre_loans6090`: number of delays from 60 to 90 days.
  - `pre_loans90`: the number of delays of more than 90 days.
  - `is_zero_loans_5`: flag: no delays up to 5 days.
  - `is_zero_loans_530`: flag: no delays from 5 to 30 days.
  - `is_zero_loans_3060`: flag: no delays from 30 to 60 days.
  - `is_zero_loans_6090`: flag: no delays from 60 to 90 days.
  - `is_zero_loans90`: flag: no delays for more than 90 days.
  - `pre_util`: ratio of the remaining unpaid loan amount to the credit limit.
  - `pre_over2limit`: ratio of current overdue debt to the credit limit.
  - `pre_maxover2limit`: ratio of the maximum overdue debt to the credit limit.
  - `is_zero_util`: flag: the ratio of the remaining unpaid loan amount to the credit limit is 0.
  - `is_zero_over2limit`: flag: the ratio of the current overdue debt to the credit limit is 0.
  - `is_zero_maxover2limit`: flag: the ratio of the maximum overdue debt to the credit limit is 0.
  - `enc_paym_{0..n}`: monthly payment statuses for the last n months.
  - `enc_loans_account_holder_type`: type of attitude to credit.
  - `enc_loans_credit_status`: loan status.
  - `enc_loans_account_cur`: loan currency.
  - `enc_loans_credit_type`: type of loan.
  - `pclose_flag`: flag: the planned number of days from the opening date of the loan to the closing date is not defined.
  - `fclose_flag`: flag: the actual number of days from the opening date of the loan to the closing date is not determined.
- `flag`: target, 1 – the fact that the client has defaulted.