migrate

Using {migrate}

This package is intended to serve as a set of tools to help convert credit risk data at two timepoints into traditional state transition matrices. At a higher level, {migrate} is intended to help an analyst understand how risk moved in their credit portfolio over a time interval.

Background

One of the more difficult aspects of making a state migration matrix in R (or Python, for that matter) is the fact that the output doesn’t satisfy the structure of a traditional data frame object. Rather, the output needs to be a matrix, which is a data structure that R does support. In the past, there has been difficulty converting a matrix to something more visual-friendly. More recently, however, tools like the kableExtra and gt packages allow us to present visually appealing output that extends the structure of a data frame. Using the matrix-style output of {migrate}’s functions with a visual formatting package such as the two mentioned above will hopefully help analysts streamline the presentation of their credit portfolio’s state migration matrices to an audience.

Getting Started

If you haven’t done so already, first install {migrate} with the instructions in the README section.

First, load the package using library()

library(migrate)

The package has a built-in mock dataset, which can be loaded into the environment like so:

data("mock_credit")

head(mock_credit[order(mock_credit$customer_id), ])   # sort by 'customer_id'

customer_id	date	risk_rating	principal_balance
Customer_1001	2020-06-30	A	915000
Customer_1001	2020-09-30	A	1328000
Customer_1002	2020-06-30	AAA	979000
Customer_1002	2020-09-30	AAA	354000
Customer_1003	2020-06-30	BBB	1400000
Customer_1003	2020-09-30	BBB	356000

Note that an important feature of the mock_credit dataset is that there are exactly two (2) unique values in the date column variable; if the time argument passed to migrate() has more than two (2) unique values, the function will throw an error.

unique(mock_credit$date)
#> [1] "2020-06-30" "2020-09-30"

To summarize the migration within the data, use the migrate() function

migrated_df <- migrate(
  data = mock_credit,
  id = customer_id,
  time = date,
  state = risk_rating,
)
#> ℹ Migrating from 2020-06-30 to 2020-09-30
head(migrated_df)
#> # A tibble: 6 × 3
#>   risk_rating_start risk_rating_end   prop
#>   <ord>             <ord>            <dbl>
#> 1 AAA               AAA             0.774 
#> 2 AAA               AA              0.194 
#> 3 AAA               A               0.0323
#> 4 AAA               BBB             0     
#> 5 AAA               BB              0     
#> 6 AAA               B               0

To create the state transition matrix, use the build_matrix() function

build_matrix(migrated_df)
#> ℹ Using `risk_rating_start` as the 'state_start' column variable
#> ℹ Using `risk_rating_end` as the 'state_end' column variable
#> ℹ Using `prop` as the 'metric' column variable
#>             AAA         AA          A        BBB         BB          B        CCC
#> AAA 0.774193548 0.19354839 0.03225806 0.00000000 0.00000000 0.00000000 0.00000000
#> AA  0.101123596 0.66292135 0.15730337 0.07865169 0.00000000 0.00000000 0.00000000
#> A   0.008333333 0.06666667 0.72500000 0.16666667 0.03333333 0.00000000 0.00000000
#> BBB 0.000000000 0.00000000 0.11363636 0.68181818 0.14772727 0.05681818 0.00000000
#> BB  0.000000000 0.00000000 0.00000000 0.11392405 0.63291139 0.16455696 0.08860759
#> B   0.000000000 0.00000000 0.00000000 0.01388889 0.09722222 0.62500000 0.26388889
#> CCC 0.000000000 0.00000000 0.00000000 0.00000000 0.00000000 0.14285714 0.85714286

Or, to do it all in one shot, use the |>

mock_credit |>
  migrate(
    id = customer_id,
    time = date,
    state = risk_rating,
    metric = principal_balance,
    percent = FALSE,
    verbose = FALSE
  ) |>
  build_matrix(
    state_start = risk_rating_start,
    state_end = risk_rating_end,
    metric = principal_balance
  )
#>          AAA       AA        A      BBB       BB        B      CCC
#> AAA 29042000  6575000    20000        0        0        0        0
#> AA   6445000 58095000 13045000 14467000        0        0        0
#> A     804000  7898000 85330000 21015000  5829000        0        0
#> BBB        0        0 12461000 65315000 13911000  8140000        0
#> BB         0        0        0 11374000 45986000 14057000  5723000
#> B          0        0        0   413000  6700000 47402000 17132000
#> CCC        0        0        0        0        0  2094000 14843000

Handle IDs with observations at a single timepoint

The following code creates a dataframe that features 500 customers with the following characteristics:

470 customers have a value at both timepoints
20 customers have a value only at the first timepoint
10 customers have a value only at the second timepoint

mock_credit_with_missing <- mock_credit |>
  # Remove the value at the first timepoint for 10 customers
  dplyr::slice(-(1:10)) |>
  # Remove the value at the last timepoint for 20 customers
  dplyr::slice(-((dplyr::n() - 19):dplyr::n()))

Check that the new dataframe has information about 500 customers:

# Number of unique customer_id values in mock_credit_with_missing
dplyr::n_distinct(mock_credit_with_missing$customer_id)
#> [1] 500

By default, migrate() drops observations that belong to IDs found at a single timepoint. migrate() informs such behavior through a warning:

migrated_data_without_fill_state <- mock_credit_with_missing |>
  migrate(
    id = customer_id,
    time = date,
    state = risk_rating,
    percent = FALSE,
    verbose = FALSE
  )
#> Warning: ! Removed 30 observations due to missingness or IDs only existing at one `time` value

Notice that only 470 customers have been migrated:

migrated_data_without_fill_state |>
  dplyr::pull(count) |>
  sum()
#> [1] 470

You can use migrate()’s fill_state argument to ensure that no information is lost during the migration process. When a filler state value (e.g., a character string such as “No Rating” or “NR”) is assigned to fill_state, IDs with a single timepoint are not removed but rather migrated from or to this filler state.

When verbose = TRUE a message will provide additional information about the IDs with missing timepoints:

migrated_data_with_fill_state <- mock_credit_with_missing |>
  migrate(
    id = customer_id,
    time = date,
    state = risk_rating,
    fill_state = "No Rating",
    percent = FALSE,
    verbose = TRUE
  )
#> ℹ Migrating from 2020-06-30 to 2020-09-30
#> ℹ 30 IDs have a missing timepoint:
#>   • Migrating 20 IDs with missing end timepoint to new class 'No Rating'
#>   • Migrating 10 IDs with missing start timepoint from new class 'No Rating'

Check that 500 customers were migrated:

migrated_data_with_fill_state |>
  dplyr::pull(count) |>
  sum()
#> [1] 500

So far we have been using count as the metric to easily determine the amount of customers that migrated in each scenario. The following code provides an example migration that leverages principal_balance as the metric:

mock_credit_with_missing |>
  migrate(
    id = customer_id,
    time = date,
    state = risk_rating,
    metric = principal_balance,
    fill_state = "No Rating",
    percent = FALSE,
    verbose = FALSE
  ) |>
  build_matrix(
    state_start = risk_rating_start,
    state_end = risk_rating_end,
    metric = principal_balance
  )
#>                AAA       AA        A      BBB       BB        B      CCC No Rating
#> AAA       27403000  4301000    20000        0        0        0        0   2274000
#> AA         6445000 56333000 11642000 14467000        0        0        0   1762000
#> A           804000  7682000 82044000 19823000  5829000        0        0   3779000
#> BBB              0        0 12461000 62071000 11480000  8140000        0   4275000
#> BB               0        0        0 10930000 44487000 11838000  5723000   1995000
#> B                0        0        0        0  6700000 46412000 14977000   3210000
#> CCC              0        0        0        0        0  2094000 14447000         0
#> No Rating        0        0        0        0        0        0        0         0