Supervisory Banking Statistics

Data - ECB

Author

François Geerolf 

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Info

Last observation: Quarterly: 2026-Q1 (N = 21,498) · NA: 2025-S2 (N = 2,626)

First observation: Quarterly: 2015-Q2 (N = 5,455) · NA: 2018-S1 (N = 840)

Last data update: 13 aoû 2026, 07:25. Last compile: 13 aoû 2026, 08:38

Structure

Info

source dataset Title .html .rData
ecb SUP Supervisory Banking Statistics 2026-08-12 2026-08-12

Data on monetary policy

source dataset Title Download Compile
ecb SUP Supervisory Banking Statistics NA [2026-08-12]
bdf FM Marché financier, taux 2026-07-22 [2026-08-12]
bdf MIR Taux d'intérêt - Zone euro NA [2026-08-12]
bdf MIR1 Taux d'intérêt - France 2026-07-23 [2026-08-12]
bis CBPOL Policy Rates, Daily 2026-08-12 [2026-08-12]
ecb BSI Balance Sheet Items NA [2026-08-13]
ecb BSI_PUB Balance Sheet Items - Published series NA [2026-08-13]
ecb FM Financial market data NA [2026-08-13]
ecb ILM Internal Liquidity Management NA [2026-08-13]
ecb ILM_PUB Internal Liquidity Management - Published series NA [2026-08-13]
ecb MIR MFI Interest Rate Statistics NA [2026-08-13]
ecb RAI Risk Assessment Indicators NA [2026-08-13]
ecb YC Financial market data - yield curve NA [2026-08-12]
ecb YC_PUB Financial market data - yield curve - Published series NA [2026-08-12]
ecb liq_daily Daily Liquidity NA [2026-08-13]
eurostat ei_mfir_m Interest rates - monthly data NA [2026-08-13]
eurostat irt_st_m Money market interest rates - monthly data NA [2026-08-13]
fred r Interest Rates NA [2026-08-12]
oecd MEI Main Economic Indicators NA [2026-08-13]
oecd MEI_FIN Monthly Monetary and Financial Statistics (MEI) NA [2026-08-13]

LAST_COMPILE

LAST_COMPILE
2026-08-13

Last

Code 
SUP |>
  group_by(TIME_PERIOD, FREQ) |>
  summarise(Nobs = n()) |>
  ungroup() |>
  group_by(FREQ) |>
  arrange(desc(TIME_PERIOD)) |>
  filter(row_number() == 1) |>
  print_table_conditional()
TIME_PERIOD FREQ Nobs
2026-Q1 Q 21498
2025-S2 H 2626

Info

  • Data Structure Definition (DSD). html

TITLE

Code 
SUP |>
  group_by(TITLE) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

BS_SUFFIX

Code 
SUP |>
  group_by(BS_SUFFIX, Bs_suffix) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
BS_SUFFIX Bs_suffix Nobs
E Euro 424266
PCT Percentage 133784
LAF Euro, linearly annualised flows 36849
Z Not applicable 17856

CB_EXP_TYPE

Code 
SUP |>
  group_by(CB_EXP_TYPE, Cb_exp_type) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
CB_EXP_TYPE Cb_exp_type Nobs
ALL All exposures 420551
_Z Not applicable 138271
N_ Non-performing exposures 22768
ST2 Assets with significant increase in credit risk since initial recognition but not credit-impaired (Stage 2) 11516
P_ Performing exposures 5114
NFM Non-performing exposures with forbearance measures 4456
PFM Performing exposures with forbearance measures 4449
ST1 Assets without significant increase in credit risk since initial recognition (Stage 1) 1986
ST3 Credit-impaired assets (Stage 3) 1986
PCI Purchased or originated credit-impaired financial assets 1658

CB_ITEM

Code 
SUP |>
  group_by(CB_ITEM, Cb_item) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

SBS_DI_1

Code 
SUP |>
  group_by(SBS_DI_1, Sbs_di_1) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
SBS_DI_1 Sbs_di_1 Nobs
SII Significant institutions 423832
LSI Less significant institutions 154827
ALL All supervised institutions 34096

SBS_BREAKDOWN

Code 
SUP |>
  group_by(SBS_BREAKDOWN, Sbs_breakdown) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
SBS_BREAKDOWN Sbs_breakdown Nobs
_T Total 365618
AMC Classification by business model - asset managers & custodians 15648
CWH Classification by business model - corporate/wholesale lenders 15648
DEV Classification by business model - development/promotional lenders 15648
DIV Classification by business model - diversified lenders 15648
NC Classification by business model - others/ not classified 15648
RCCL Classification by business model - retail lenders and consumer credit lenders 15648
UNI Classification by business model - universal and investment banks 15648
GSIB Classification by size/business model - G-SIBs 11120
SML Classification by business model - small market lenders 10763
SL30 Classification by size - banks with total assets less than 30 billion of EUR 9698
SM20 Classification by size - banks with total assets more than 200 billion of EUR 9698
ST10 Classification by size - banks with total assets between 30 billion and 100 billion of EUR 9698
ST20 Classification by size - banks with total assets between 100 billion and 200 billion of EUR 9698
LORI Classification by risk - banks with low risk 9341
MHRI Classification by risk - banks with medium, high risk and non-rated 9341
DOM Classification by geographical diversification - banks with significant domestic exposures 9292
EEA Classification by geographical diversification - banks with largest non-domestic exposures in non-SSM EEA 9292
NEEA Classification by geographical diversification - banks with largest non-domestic exposures in non-EEA Europe 9292
ROW Classification by geographical diversification - banks with largest non-domestic exposures in RoW 9292
SSM Classification by geographical diversification - banks with largest non-domestic exposures in the SSM 9292
CSCB Classification by business model-central savings and cooperative banks 5892
EML Classification by business model-emerging markets lenders 5892

COUNT_AREA

Code 
SUP |>
  group_by(COUNT_AREA, Count_area) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

FREQ

Code 
SUP |>
  group_by(FREQ, Freq) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
FREQ Freq Nobs
Q Quarterly 586695
H Half-yearly 26060

REF_AREA

Code 
SUP |>
  group_by(REF_AREA, Ref_area) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
REF_AREA Ref_area Nobs
B01 EU countries participating in the Single Supervisory Mechanism (SSM) (changing composition) 280979
AT Austria 16188
BE Belgium 16188
CY Cyprus 16188
DE Germany 16188
EE Estonia 16188
ES Spain 16188
FI Finland 16188
FR France 16188
GR Greece 16188
IE Ireland 16188
IT Italy 16188
LT Lithuania 16188
LU Luxembourg 16188
LV Latvia 16188
MT Malta 16188
NL Netherlands 16188
PT Portugal 16188
SI Slovenia 16188
SK Slovakia 16188
BG Bulgaria 12102
HR Croatia 12102

COUNTERPART_SECTOR

Code 
SUP |>
  group_by(COUNTERPART_SECTOR, Counterpart_sector) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
COUNTERPART_SECTOR Counterpart_sector Nobs
_Z Not applicable 480603
S13 General government 34831
S11 Non financial corporations 29612
S14 Households 27706
S12R Other financial corporations 14412
S122Z Deposit-taking corporations except the central bank and excluding electronic money institutions principally engaged in financial intermediation 8764
S121 Central bank 8733
S1V Non-financial corporations, households and NPISH 8094

TIME_FORMAT

Code 
SUP |>
  group_by(TIME_FORMAT) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
TIME_FORMAT Nobs
P3M 586695
P6M 26060

Performance Indicators

https://www.bankingsupervision.europa.eu/banking/statistics/html/index.en.html

Return on equity

Graph

significant institutions (SIs) and less significant institutions (LSIs):

Code 
SUP |>
  filter(grepl("Return on equity", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") |>
  quarter_to_date() %>%
  select_if(~ n_distinct(.) > 1) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Return on equity") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

Cost-to-income ratio

Code 
SUP |>
  filter(grepl("Cost-to-income ratio", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") |>
  quarter_to_date() %>%
  select_if(~ n_distinct(.) > 1) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Cost-to-income Ratio") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 10),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

Net interest income

FR, IT, DE

Code 
SUP |>
  filter(grepl("Net interest income", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") |>
  quarter_to_date() %>%
  select_if(~ n_distinct(.) > 1) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net Interest Income/Total operating income") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 10),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, SI

Net interest margin

FR, IT, DE

Code 
SUP |>
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE")) |>
  quarter_to_date() %>%
  select_if(~ n_distinct(.) > 1) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, SI

Code 
SUP |>
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "SI"),
         SBS_DI_1 == "SII") |>
  quarter_to_date() |>
  arrange(desc(date)) |>
  select_if(~ n_distinct(.) > 1) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, BE, AT, PT

2015-

Code 
SUP |>
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "BE", "AT", "PT"),
         SBS_DI_1 == "SII") |>
  quarter_to_date() |>
  arrange(desc(date)) |>
  select_if(~ n_distinct(.) > 1) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(8) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

2018-

Code 
SUP |>
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "BE", "AT", "PT"),
         SBS_DI_1 == "SII") |>
  quarter_to_date() |>
  filter(date >= as.Date("2018-01-01")) |>
  arrange(desc(date)) |>
  select_if(~ n_distinct(.) > 1) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(8) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

Liquidity

Liquidity coverage ratios (LCR)

The LCR is the percentage resulting from dividing the bank’s stock of high-quality assets by the estimated total net cash outflows over a 30 calendar day stress scenario.

Code 
SUP |>
  filter(CB_ITEM == "I3017",
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         !(SBS_DI_1 == "_Z")) |>
  quarter_to_date() %>%
  select_if(~ n_distinct(.) > 1) |>
  arrange(desc(date)) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Liquidity coverage ratio") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 500, 50),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 3.5)) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

Liquidity buffer

Code 
SUP |>
  filter(CB_ITEM == "A6310",
         REF_AREA %in% c("U2", "FR", "IT", "DE")) |>
  quarter_to_date() %>%
  select_if(~ n_distinct(.) > 1) |>
  arrange(desc(date)) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Liquidity buffer") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000, 2)) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

Net liquidity outflow

Code 
SUP |>
  filter(CB_ITEM == "A6320",
         REF_AREA %in% c("U2", "FR", "IT", "DE")) |>
  quarter_to_date() %>%
  select_if(~ n_distinct(.) > 1) |>
  arrange(desc(date)) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  mutate(OBS_VALUE = OBS_VALUE/100) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net liquidity outflow") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000, 2)) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

Net liquidity outflow

Code 
SUP |>
  filter(grepl("Net liquidity outflow", TITLE),
         REF_AREA %in% c("B01", "FR", "IT", "DE")) |>
  quarter_to_date() |>
  mutate(OBS_VALUE = OBS_VALUE/100,
         Ref_area = ifelse(REF_AREA == "B01", "Europe", Ref_area)) %>%
  select_if(~ n_distinct(.) > 1) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  na.omit() |>
  ggplot() + theme_minimal() + xlab("") + ylab("Net liquidity outflow") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = paste0(SBS_DI_1, SBS_BREAKDOWN))) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 500, 50),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 3.5)) +
  theme(legend.position = c(0.55, 0.50),
        legend.title = element_blank())

Net Liquidity outflow - EU

B01 - EU countries participating in the Single Supervisory Mechanism (SSM)

Capital adequacy

  • Common equity Tier 1 ratio

  • Tier 1 ratio

https://www.bankingsupervision.europa.eu/press/pr/date/2023/html/ssm.pr2301114cb4953fd6.en.html#::text=The%20aggregate%20capital%20ratios%20of,capital%20ratio%20stood%20at%2018.68%25.