diff --git a/NEWS.md b/NEWS.md
index 226629b7..9990ed9e 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,7 +1,10 @@
 # r2dii.analysis (development version)
 
 * `target_market_share()` now correctly outputs `technology_share` with
-  multiple loans to the same company (@georgeharris2deg #262).
+  multiple loans to the same company (@georgeharris2deg #262, @ab-bbva #265).
+  
+* `target_market_share()` now errors if input `data` has an unexpected column
+  (@georgeharris2deg #267).
 
 # r2dii.analysis 0.1.4
 
diff --git a/R/target_market_share.R b/R/target_market_share.R
index bb7ff169..f3b2b346 100644
--- a/R/target_market_share.R
+++ b/R/target_market_share.R
@@ -94,7 +94,7 @@ target_market_share <- function(data,
   }
 
   data <- ungroup(warn_grouped(data, "Ungrouping input data."))
-
+  check_unexpected_columns(data)
   data <- aggregate_by_loan_id(data)
 
   crucial_scenario <- c("scenario", "tmsr", "smsp")
@@ -390,10 +390,15 @@ reweight_technology_share <- function(data, ...) {
 }
 
 aggregate_by_loan_id <- function(data) {
-  aggregate_columns <- c("id_loan", "loan_size_outstanding", "loan_size_credit_limit")
 
   data %>%
-    dplyr::group_by_at(setdiff(names(data), aggregate_columns)) %>%
+    group_by(
+      .data$level,
+      .data$loan_size_outstanding_currency,
+      .data$loan_size_credit_limit_currency,
+      .data$name_ald,
+      .data$sector_ald
+      ) %>%
     summarize(
       id_loan = first(.data$id_loan),
       loan_size_outstanding = sum(.data$loan_size_outstanding),
@@ -401,3 +406,49 @@ aggregate_by_loan_id <- function(data) {
     ) %>%
     ungroup()
 }
+
+check_unexpected_columns <- function(data) {
+
+  possible_matched_columns <- c(
+    "id_loan",
+    "id_direct_loantaker",
+    "name_direct_loantaker",
+    "id_intermediate_parent_1",
+    "name_intermediate_parent_1",
+    "id_ultimate_parent",
+    "name_ultimate_parent",
+    "loan_size_outstanding",
+    "loan_size_outstanding_currency",
+    "loan_size_credit_limit",
+    "loan_size_credit_limit_currency",
+    "sector_classification_system",
+    "sector_classification_input_type",
+    "sector_classification_direct_loantaker",
+    "fi_type",
+    "flag_project_finance_loan",
+    "name_project",
+    "lei_direct_loantaker",
+    "isin_direct_loantaker",
+    "id_2dii",
+    "level",
+    "sector",
+    "sector_ald",
+    "name",
+    "name_ald",
+    "score",
+    "source",
+    "borderline"
+    )
+
+unexpected_names <- setdiff(names(data), possible_matched_columns)
+
+  if (length(unexpected_names) != 0) {
+    abort(
+      glue("Loanbook has unexpected names: `{unexpected_names}`."),
+      class = "unexpected_names"
+    )
+  }
+
+  invisible(data)
+
+}
diff --git a/tests/testthat/test-target_market_share.R b/tests/testthat/test-target_market_share.R
index 99d5f02b..3b089964 100644
--- a/tests/testthat/test-target_market_share.R
+++ b/tests/testthat/test-target_market_share.R
@@ -676,3 +676,68 @@ test_that("for one company with multiple loans of different size, unweighted
 
   expect_equal(projected$production, fake_ald()$production)
 })
+
+test_that("with bad column errors with informative message (#267)", {
+
+  bad_matched <- fake_matched(
+    bad_column = "bad"
+  )
+
+  expect_error(
+    class = "unexpected_names",
+    target_market_share(
+      bad_matched,
+      fake_ald(),
+      fake_scenario()
+    )
+  )
+})
+
+test_that("`technology_share` outputs consistently when multiple
+          direct_loantakers match to a single company (#265)", {
+
+  matched <- fake_matched(
+    id_loan = c("L1", "L2", "L3", "L4", "L5"),
+    name_ald = c(rep("company a", 4), "company b")
+  )
+
+  matched_split_dl <- matched %>%
+    mutate(name_direct_loantaker = c("company a1", "company a2", "company a3", "company a4", "company b"))
+
+  ald <- fake_ald(
+    name_company = rep(c("company a","company b"), each = 2),
+    technology = rep(c("ice", "electric"), 2),
+    production = c(8, 2, 15, 5)
+  )
+
+  scenario <- fake_scenario(
+    technology = c("ice", "electric")
+  )
+
+  out <- target_market_share(
+    matched,
+    ald,
+    scenario,
+    region_isos_stable
+  ) %>%
+    filter(
+      metric == "projected",
+      year == 2025,
+      technology == "ice"
+    )
+
+  out_split_dl <- target_market_share(
+    matched_split_dl,
+    ald,
+    scenario,
+    region_isos_stable
+  ) %>%
+    filter(
+      metric == "projected",
+      year == 2025,
+      technology == "ice"
+    )
+
+  expect_equal(out$technology_share, out_split_dl$technology_share)
+
+})