Match a loanbook to asset-based company data (abcd) by the name_* columns

match_name() scores the match between names in a loanbook dataset (columns can be name_direct_loantaker, name_intermediate_parent* and name_ultimate_parent) with names in an asset-based company data (column name_company). The raw names are first internally transformed, and aliases are assigned. The similarity between aliases in each of the loanbook and abcd is scored using stringdist::stringsim().

Usage

match_name(
  loanbook,
  abcd,
  by_sector = TRUE,
  min_score = 0.8,
  method = "jw",
  p = 0.1,
  overwrite = NULL,
  join_id = NULL,
  sector_classification = default_sector_classification(),
  ...
)

Arguments

loanbook, abcd: data frames structured like r2dii.data::loanbook_demo and r2dii.data::abcd_demo.
by_sector: Should names only be compared if companies belong to the same sector?
min_score: A number between 0-1, to set the minimum score threshold. A score of 1 is a perfect match.
method: Method for distance calculation. One of c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw", "soundex"). See stringdist::stringdist-metrics.
p: Prefix factor for Jaro-Winkler distance. The valid range for p is 0 <= p <= 0.25. If p=0 (default), the Jaro-distance is returned. Applies only to method='jw'.
overwrite: A data frame used to overwrite the sector and/or name columns of a particular direct loantaker or ultimate parent. To overwrite only sector, the value in the name column should be NA and vice-versa. This file can be used to manually match loanbook companies to abcd.
join_id: A join specification passed to dplyr::inner_join(). If a character string, it assumes identical join columns between loanbook and abcd. If a named character vector, it uses the name as the join column of loanbook and the value as the join column of abcd.
sector_classification: A data frame containing sector classifications in the same format as r2dii.data::sector_classifications. The default value is r2dii.data::sector_classifications.
...: Arguments passed on to stringdist::stringsim().

Value

A data frame with the same groups (if any) and columns as loanbook, and the additional columns:

id_2dii - an id used internally by match_name() to distinguish companies
level - the level of granularity that the loan was matched at (e.g direct_loantaker or ultimate_parent)
sector - the sector of the loanbook company
sector_abcd - the sector of the abcd company
name - the name of the loanbook company
name_abcd - the name of the abcd company
score - the score of the match (manually set this to 1 prior to calling prioritize() to validate the match)
source - determines the source of the match. (equal to loanbook unless the match is from overwrite

The returned rows depend on the argument min_value and the result of the column score for each loan: * If any row has score equal to 1, match_name() returns all rows where score equals 1, dropping all other rows. * If no row has score equal to 1,match_name() returns all rows where score is equal to or greater than min_score. * If there is no match the output is a 0-row tibble with the expected column names – for type stability.

Assigning aliases

The transformation process used to compare names between loanbook and abcd datasets applies best practices commonly used in name matching algorithms:

Remove special characters.
Replace language specific characters.
Abbreviate certain names to reduce their importance in the matching.
Spell out numbers to increase their importance.

Handling grouped data

This function ignores but preserves existing groups.

Examples

library(r2dii.data)
#> 
#> Attaching package: ‘r2dii.data’
#> The following object is masked from ‘package:pacta.loanbook’:
#> 
#>     data_dictionary
library(tibble)

# Small data for examples
loanbook <- head(loanbook_demo, 50)
abcd <- head(abcd_demo, 50)

match_name(loanbook, abcd)
#> # A tibble: 6 × 22
#>   id_loan id_direct_loantaker name_direct_loantaker           id_ultimate_parent
#>   <chr>   <chr>               <chr>                           <chr>             
#> 1 L14     C296                Gallo e figli                   UP3               
#> 2 L17     C290                De luca, De luca e De luca e f… UP6               
#> 3 L21     C286                Gallo Group                     UP63              
#> 4 L30     C274                Sanna SPA                       UP33              
#> 5 L31     C273                Caputo SPA                      UP5               
#> 6 L48     C256                Hellwig AG                      UP201             
#> # ℹ 18 more variables: name_ultimate_parent <chr>, loan_size_outstanding <dbl>,
#> #   loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>,
#> #   loan_size_credit_limit_currency <chr>, sector_classification_system <chr>,
#> #   sector_classification_direct_loantaker <chr>, lei_direct_loantaker <chr>,
#> #   isin_direct_loantaker <chr>, id_2dii <chr>, level <chr>, sector <chr>,
#> #   sector_abcd <chr>, name <chr>, name_abcd <chr>, score <dbl>, source <chr>,
#> #   borderline <lgl>

match_name(loanbook, abcd, min_score = 0.9)
#> # A tibble: 3 × 22
#>   id_loan id_direct_loantaker name_direct_loantaker           id_ultimate_parent
#>   <chr>   <chr>               <chr>                           <chr>             
#> 1 L14     C296                Gallo e figli                   UP3               
#> 2 L17     C290                De luca, De luca e De luca e f… UP6               
#> 3 L31     C273                Caputo SPA                      UP5               
#> # ℹ 18 more variables: name_ultimate_parent <chr>, loan_size_outstanding <dbl>,
#> #   loan_size_outstanding_currency <chr>, loan_size_credit_limit <dbl>,
#> #   loan_size_credit_limit_currency <chr>, sector_classification_system <chr>,
#> #   sector_classification_direct_loantaker <chr>, lei_direct_loantaker <chr>,
#> #   isin_direct_loantaker <chr>, id_2dii <chr>, level <chr>, sector <chr>,
#> #   sector_abcd <chr>, name <chr>, name_abcd <chr>, score <dbl>, source <chr>,
#> #   borderline <lgl>

# match on LEI
loanbook <- tibble(
  sector_classification_system = "NACE",
  sector_classification_direct_loantaker = "D35.11",
  id_ultimate_parent = "UP15",
  name_ultimate_parent = "Won't fuzzy match",
  id_direct_loantaker = "C294",
  name_direct_loantaker = "Won't fuzzy match",
  lei_direct_loantaker = "LEI123"
)

abcd <- tibble(
  name_company = "alpine knits india pvt. limited",
  sector = "power",
  lei = "LEI123"
)

match_name(loanbook, abcd, join_id = c(lei_direct_loantaker = "lei"))
#> # A tibble: 1 × 15
#>   sector_classification_system sector_classification_direct…¹ id_ultimate_parent
#>   <chr>                        <chr>                          <chr>             
#> 1 NACE                         D35.11                         UP15              
#> # ℹ abbreviated name: ¹sector_classification_direct_loantaker
#> # ℹ 12 more variables: name_ultimate_parent <chr>, id_direct_loantaker <chr>,
#> #   name_direct_loantaker <chr>, lei_direct_loantaker <chr>, sector <chr>,
#> #   borderline <lgl>, name_abcd <chr>, sector_abcd <chr>, score <dbl>,
#> #   source <chr>, level <chr>, name <chr>

# Use your own `sector_classifications`
your_classifications <- tibble(
  sector = "power",
  borderline = FALSE,
  code = "D35.11",
  code_system = "XYZ"
)

loanbook <- tibble(
  sector_classification_system = "XYZ",
  sector_classification_direct_loantaker = "D35.11",
  id_ultimate_parent = "UP15",
  name_ultimate_parent = "Alpine Knits India Pvt. Limited",
  id_direct_loantaker = "C294",
  name_direct_loantaker = "Yuamen Xinneng Thermal Power Co Ltd"
)

abcd <- tibble(
  name_company = "alpine knits india pvt. limited",
  sector = "power"
)

match_name(loanbook, abcd, sector_classification = your_classifications)
#> # A tibble: 1 × 15
#>   sector_classification_system sector_classification_direct…¹ id_ultimate_parent
#>   <chr>                        <chr>                          <chr>             
#> 1 XYZ                          D35.11                         UP15              
#> # ℹ abbreviated name: ¹sector_classification_direct_loantaker
#> # ℹ 12 more variables: name_ultimate_parent <chr>, id_direct_loantaker <chr>,
#> #   name_direct_loantaker <chr>, id_2dii <chr>, level <chr>, sector <chr>,
#> #   sector_abcd <chr>, name <chr>, name_abcd <chr>, score <dbl>, source <chr>,
#> #   borderline <lgl>

Match a loanbook to asset-based company data (abcd) by the `name_*` columns