Retail risk indicators interactive tool
Article 9(1) of Regulation (EU) no 1093/2010 (“EBA Regulation”) requires the EBA to develop retail risk indicators (RRIs) for the timely identification of potential consumer harm. For this purpose, the EBA is publishing a list of 11 RRIs that cover a wide variety of different types of products in the EBA’s remit (e.g., mortgage credit, consumer credit, or payment accounts).
These indicators aim to facilitate the monitoring of the banking markets across the EU by measuring the risk of detriment arising to consumers from the misconduct of the institutions, and from wider economic conditions.
They provide information that help the EBA and national competent authorities to prioritise their regulatory and supervisory work in the area of consumer protection but may be of interest to other, external stakeholders as well. The RRI are summarized in a table below, which is accompanied by a set of charts showing results at Member State-level, and a methodological note explaining the interpretation of the results, which should be read alongside it.
The data used to calculate the indicators has a number of limitations that have an impact on how these indicators should be interpreted. Amongst those that apply more generally across several of said indicators is the limitation that some indicators do not cover all EU and other EEA countries. Further, in the absence of comprehensive consumer protection data being available for the EU, some indicators are calculated based on data collected by the EBA primarily for the purpose of prudential supervision rather than consumer protection requirements, and thus should be understood as mere proxies for potential consumer detriment, not precise metrics. Finally, many of these indicators are published for the first time, and any potential trends will only emerge in the coming years.
These limitations are particularly important when comparing Member States against one another, where the aforementioned limitations are more likely to make it difficult to arrive at robust conclusions as results may reflect market and/or business model specificities. For that reason, direct comparison of results between specific Member States should be done very cautiously and should be done in the wider context of a given banking market and the Member State’s economic circumstances. The main purpose of showing Member State level data is to demonstrate the divergence across all EU Member States. These divergences can be significant and are an interesting observation in their own right for the purpose of future prioritization of tasks. Future improvements in the comprehensiveness and quality of the data will gradually improve the robustness of these indicators over time.
Following its initial publication in Q1 of 2023, the indicators will subsequently be updated and refined on an annual basis and published as part of the EBA’s annual Risk Assessment Report.
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Name of indicator | Indicator number | Value – EU/EEA average | Reference period | |
I. Mortgage credits | Share of household loans with forbearance measures over total household loans | MC1 | 1.5% (1.7%) | 30/06/2023 (30/06/2022) |
Share of NPLs collateralised by immovable property over total loans collateralised by immovable property | MC2 | 1.5% (1.5%) | 30/06/2023 (30/06/2022) | |
II. Other consumer loans | Share of NPLs from credits for consumption over total credits for consumption | OCL1 | 5.2% (5.3%) | 30/06/2023 (30/06/2022) |
III. Payment and deposit accounts | Percentage of deposit interest expenses paid by banks to households over total household deposits | PDA1 | 0.5% (0.2%) | 30/06/2023 (30/06/2022) |
IV. Credit & debit cards | Share of fraudulent card payments over total card payments (in terms of volume and value of total transactions) | CDC1 | 0.015% - volume | 2022 |
0.027% - value | 2022 | |||
Change to previous year of the fraud losses borne by card payment users | CDC2 | 49% | Difference between 2021 and 2022 | |
V. Other payment instruments | Share of fraudulent credit transfer payments over total transfer payments (in terms of volume and value of total transactions) | OPI1 | 0.0026% - volume | 2022 |
0.0006% - value | 2022 | |||
Change to previous year of the fraud losses borne by consumers (credit transfers) | OPI2 | 64% | Difference between 2021 and 2022 | |
VI. Access to financial services | The percentage of people aged 15+ who have an account at a bank or another type of financial institution | AFS1 | 86%/89%/91%/92% | 2011/2014/2017/2021 |
The percentage of respondents aged 15+ who report having a debit or credit card | AFS2 | 74%/78%/84%/85% | 2011/2014/2017/2021 | |
The percentage of respondents aged 15+ who report borrowing any money from family, relatives, or friends in the past year | AFS3 | 13%/16%/15%/15% | 2011/2014/2017/2021 |
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
I. Mortgage credits | Access of consumers to forbearance measures | MC1 | (Ax100)/B |
Data source: FINREP/COREP
Formula used to calculate the indicator: (A x 100)/B, where A = Exposures with forbearance measures for loans and advances for households, and B = total loans and advances to households on balance sheet. EEA weighted average is calculated by dividing the sum of all MS numerator data by the sum of all MS denominator data.
Member State-level data is at the highest level of consolidation in the Member State. This means that at Member State level, the subsidiary located in Member State X of a bank headquartered in Member State Y is included in data for both Member States X and Y.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
I. Mortgage credits | NPLs of collateralised by immovable property over all loans collateralised by immovable property | MC2 | (A x 100)/B |
Data source: FINREP/COREP
Formula used to calculate the indicator: (A x 100)/B, where A = Exposures with forbearance measures for loans and advances for households, and B = total loans and advances to households on balance sheet. EEA weighted average is calculated by dividing the sum of all MS numerator data by the sum of all MS denominator data.
Member State-level data is at the highest level of consolidation in the Member State. This means that at Member State level, the subsidiary located in Member State X of a bank headquartered in Member State Y is included in data for both Member States X and Y.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
II. Other consumer loans | NPLs from credits for consumption over all credits for consumption | OCL1 | (A x 100)/B |
Data source: FINREP/COREP
Formula used to calculate the indicator: (A x 100)/B, where A = Non-performing loans and advances for credit for consumption, and B = Total gross loans and advances for credit for consumption. EEA weighted average is calculated by dividing the sum of all MS numerator data by the sum of all MS denominator data.
Note: Member State-level data is at the highest level of consolidation in the Member State. This means that at Member State level, the subsidiary located in Member State X of a bank headquartered in Member State Y is included in data for both Member States X and Y.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
III. Payment and deposit accounts | PDA1 | (A x 100)/ B |
Data source: FINREP/COREP
Formula used to calculate the indicator:(A x 100)/B A: Interest paid by banks to households on their deposits (annualized) B: Total households’ deposits (average). EEA weighted average is calculated by dividing the sum of all MS numerator data by the sum of all MS denominator data.
Note: Member State-level data is at the highest level of consolidation in the Member State. This means that at Member State level, the subsidiary located in Member State X of a bank headquartered in Member State Y is included in data for both Member States X and Y.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
IV. Credit & debit cards | Share of fraudulent card payments over all card payments (in terms of volume and value of total transactions) | CDC1 | (A x 100) / B |
| (A x 100) / B A: Value of fraudulent payments B: Value of total payments |
Data source: EBA
Formula used to calculate the indicator: (A x 100) / B, where A = Volume of fraudulent card payments, and B = Volume of total card payments. And (A x 100) / B, where A = Value of fraudulent card payments, and B = Value of total card payments. Weighted average EU MS sample is calculated by dividing the sum of all MS numerator data (multiplied by 100) by the sum of all MS denominator data.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
IV. Credit & debit cards | Change to previous year of the fraud losses borne by card payment users | CDC2 | (100 x (A - B)) / B |
Data source: EBA
Formula used to calculate the indicator: (100 x (A - B)) / B, where A = Absolute value losses due to fraud borne by card payment services users Time Y, and B = Absolute value losses due to fraud borne by card payment services users Time Y – 1. Weighted average EU MS sample is calculated by dividing the sum of all MS numerator data (multiplied by 100) by the sum of all MS denominator data.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
V. Other payment instruments | Share of fraudulent credit transfer payments over all transfer payments (in terms of volume and value of total transactions) | OPI1 | (A x 100) / B |
| (A x 100) / B A: Value of fraudulent payments B: Value of total payments |
Data source: EBA
Formula used to calculate the indicator: (A x 100) / B, where A = Volume of fraudulent payments – credit transfers, and B = Volume of total payments – credit transfers. And (A x 100) / B where A = Value of fraudulent payments – credit transfers, and B = Value of total payments – credit transfers. Weighted average EU MS sample is calculated by dividing the sum of all MS numerator data (multiplied by 100) by the sum of all MS denominator data.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
V. Other payment instruments | Change to previous year of the fraud losses borne by consumers (credit transfers) | OPI2 | (100 x (A - B)) / B |
Data source: EBA
Formula used to calculate the indicator: (100 x (A - B)) / B, where A = Absolute value losses due to fraud borne by payment services users (credit transfer) Time Y, and B = Absolute value losses due to fraud borne by payment services users (credit transfer) Time Y – 1. Weighted average EU MS sample is calculated by dividing the sum of all MS numerator data (multiplied by 100) by the sum of all MS denominator data.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
IV. Access to financial services | AFS1 | Simple average of percentages reported for each EU Member State in the Global “Findex” database |
Data source: World Bank
EEA average is a simple average of average results across the EU Member States.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
IV. Access to financial services | The percentage of respondents aged 15+ who report having a debit or credit card | AFS2 | Simple average of percentages reported for each EU Member State in the Global “Findex” database |
Data source: World Bank
EEA average is a simple average of average results across the EU Member States.
Product | Name of indicator | Indicator number | Calculation formula |
|---|---|---|---|
IV. Access to financial services | AFS3 | Simple average of percentages reported for each EU Member State in the Global “Findex” database |
Data source: World Bank
EEA average is a simple average of average results across the EU Member States.