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Investment / Credit & banking

Credit & banking

Credit depth, physical access points, and asset quality across the SEA-11. Read the insight →
Separate frameworks · mixed vintages

Domestic credit to the private sector

Credit depth is not account ownership or financial inclusion. Laos’s 2010 observation is critically stale and excluded from the bars and ranking; the original value remains in the CSV.

% of GDP

WDI / IMF · MIXED VINTAGES

SEA-11 · Singapore is regional context, outside the peer five. Regional scales stay fixed when filtering.

Domestic credit to the private sector: source, year and as_of by country
Country% of GDP
0160
YearSource & notes
Thailand143.082025
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2025 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts.

Original CSV value: 143.08365214331.

Singapore128.43†2020 · OLDER
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2020 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts. Stale vintage (2020) — badge required.

Original CSV value: 128.43285830434.

Vietnam124.96†2022 · OLDER
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2022 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts. Vintage 2022 — badge if peers are 2024–25.

Original CSV value: 124.961478822546.

Cambodia121.482025
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2025 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts.

Original CSV value: 121.482151366024.

Malaysia117.942025
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2025 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts.

Original CSV value: 117.938077066737.

Philippines52.112025
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2025 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts.

Original CSV value: 52.1093952249199.

Brunei40.152025
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2025 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts.

Original CSV value: 40.1476670566578.

Timor-Leste39.152025
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2025 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts.

Original CSV value: 39.1472446818212.

Indonesia36.282025
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2025 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts.

Original CSV value: 36.2832983305564.

MyanmarConflict / reporting caveat28.98†2020 · OLDER
World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2020 · as_of 2026-09-06
Observation notes

Domestic credit to private sector (% of GDP). Prefer for credit-depth charts. Stale vintage (2020) — badge required.

Original CSV value: 28.9828999467009.

Philippines highlighted. Bars start at zero; gaps have no bars. † identifies older observations.

Laos: †2010 · critically stale

Excluded from the current chart and peer ranking. The historical observation is retained in the CSV.

World Bank WDI FS.AST.PRVT.GD.ZS (IMF IFS) · year 2010 · as_of 2026-09-06

Commercial bank branches

Physical access points per adult population, not usage. Singapore’s low branch density does not imply shallow banking.

Branches per 100,000 adults

WDI / IMF · MIXED VINTAGES

SEA-11 · Singapore is regional context, outside the peer five. Regional scales stay fixed when filtering.

Commercial bank branches: source, year and as_of by country
CountryBranches per 100,000 adults
020
YearSource & notes
Brunei15.76†2021 · OLDER
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2021 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI). Stale vintage (2021) — badge required.

Original CSV value: 15.7600864553314.

Cambodia14.082024
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 14.0830887702144.

Indonesia11.232024
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 11.2296187378829.

Philippines8.092024
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 8.08637788193146.

Thailand8.022024
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 8.01998751107904.

Malaysia7.722024
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 7.72357928210984.

SingaporeThin branches ≠ shallow banking5.892024
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 5.88965377276402.

MyanmarConflict / reporting caveat5.82†2020 · OLDER
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2020 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI). Stale vintage (2020) — badge required.

Original CSV value: 5.82082150690233.

Timor-Leste5.27†2023 · OLDER
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2023 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 5.27212976468727.

Vietnam3.102024
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 3.09669381955115.

Laos3.022024
World Bank WDI FB.CBK.BRCH.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Commercial bank branches per 100,000 adults (IMF FAS via WDI).

Original CSV value: 3.02390154471585.

Philippines highlighted. Bars start at zero; gaps have no bars. † identifies older observations.

Automated teller machines

Physical cash-access infrastructure, not a measure of account ownership or digital payment use.

ATMs per 100,000 adults

WDI / IMF · MIXED VINTAGES

SEA-11 · Singapore is regional context, outside the peer five. Regional scales stay fixed when filtering.

Automated teller machines: source, year and as_of by country
CountryATMs per 100,000 adults
0100
YearSource & notes
Thailand93.032024
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 93.0302183963717.

Brunei70.92†2021 · OLDER
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2021 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI). Stale vintage (2021) — badge required.

Original CSV value: 70.9203890489913.

Malaysia52.172024
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 52.1737129345502.

Cambodia47.612024
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 47.6111762552663.

Singapore47.002024
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 47.0046890272186.

Indonesia45.642024
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 45.6370507881167.

Laos28.302024
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 28.3029809215782.

Philippines27.842024
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 27.8445692139854.

Vietnam27.242024
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2024 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 27.2434250522194.

Timor-Leste16.15†2023 · OLDER
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2023 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI).

Original CSV value: 16.1458974043547.

MyanmarConflict / reporting caveat7.60†2020 · OLDER
World Bank WDI FB.ATM.TOTL.P5 (IMF Financial Access Survey) · year 2020 · as_of 2026-09-06
Observation notes

Automated teller machines (ATMs) per 100,000 adults (IMF FAS via WDI). Stale vintage (2020) — badge required.

Original CSV value: 7.5998258289253.

Philippines highlighted. Bars start at zero; gaps have no bars. † identifies older observations.

Non-performing loan ratio

An asset-quality ratio, not credit depth. Laos, Myanmar and Timor-Leste have no observations in the locked pull; gaps are never zero-filled.

% of gross loans

WDI / IMF · MIXED VINTAGES

SEA-11 · Singapore is regional context, outside the peer five. Regional scales stay fixed when filtering.

Non-performing loan ratio: source, year and as_of by country
Country% of gross loans
08
YearSource & notes
Cambodia6.56†2024 · OLDER
World Bank WDI FB.AST.NPER.ZS (IMF IFS / financial soundness) · year 2024 · as_of 2026-09-06
Observation notes

Bank nonperforming loans to total gross loans (%).

Original CSV value: 6.56317584845802.

Vietnam4.85†2024 · OLDER
World Bank WDI FB.AST.NPER.ZS (IMF IFS / financial soundness) · year 2024 · as_of 2026-09-06
Observation notes

Bank nonperforming loans to total gross loans (%).

Original CSV value: 4.84808056727128.

Philippines3.022025
World Bank WDI FB.AST.NPER.ZS (IMF IFS / financial soundness) · year 2025 · as_of 2026-09-06
Observation notes

Bank nonperforming loans to total gross loans (%).

Original CSV value: 3.01861585862953.

Brunei2.82†2022 · OLDER
World Bank WDI FB.AST.NPER.ZS (IMF IFS / financial soundness) · year 2022 · as_of 2026-09-06
Observation notes

Bank nonperforming loans to total gross loans (%). Vintage 2022 — badge if peers are 2024–25.

Original CSV value: 2.81875346844338.

Thailand2.82†2024 · OLDER
World Bank WDI FB.AST.NPER.ZS (IMF IFS / financial soundness) · year 2024 · as_of 2026-09-06
Observation notes

Bank nonperforming loans to total gross loans (%).

Original CSV value: 2.8154168013744.

Indonesia1.922025
World Bank WDI FB.AST.NPER.ZS (IMF IFS / financial soundness) · year 2025 · as_of 2026-09-06
Observation notes

Bank nonperforming loans to total gross loans (%).

Original CSV value: 1.91576331529028.

Malaysia1.372025
World Bank WDI FB.AST.NPER.ZS (IMF IFS / financial soundness) · year 2025 · as_of 2026-09-06
Observation notes

Bank nonperforming loans to total gross loans (%).

Original CSV value: 1.36970902816722.

Singapore1.31†2019 · OLDER
World Bank WDI FB.AST.NPER.ZS (IMF IFS / financial soundness) · year 2019 · as_of 2026-09-06
Observation notes

Bank nonperforming loans to total gross loans (%). Stale vintage (2019) — badge required.

Original CSV value: 1.30642223339991.

Laos—GAP · not zero—
Pack coverage note · WDI FB.AST.NPER.ZS unavailable · year not available · as_of 2026-09-06
Observation notes

No non-null NPL observation in the documented 2010–2025 pull. Missing source row; not a zero.

Original CSV value: not supplied.

MyanmarConflict / reporting caveat—GAP · not zero—
Pack coverage note · WDI FB.AST.NPER.ZS unavailable · year not available · as_of 2026-09-06
Observation notes

No non-null NPL observation in the documented 2010–2025 pull. Missing source row; not a zero.

Original CSV value: not supplied.

Timor-Leste—GAP · not zero—
Pack coverage note · WDI FB.AST.NPER.ZS unavailable · year not available · as_of 2026-09-06
Observation notes

No non-null NPL observation in the documented 2010–2025 pull. Missing source row; not a zero.

Original CSV value: not supplied.

Philippines highlighted. Bars start at zero; gaps have no bars. † identifies older observations.

Key findings from this pack

  • PH credit depth exceeds Indonesia’s but trails Thailand, Malaysia and Vietnam’s older observation.
  • PH branch density is close to Thailand’s, while ATM density trails Thailand and Malaysia.
  • Singapore’s thin branch network does not establish shallow banking.
  • Laos credit is critically stale; missing NPLs remain gaps. Credit depth and Findex inclusion are different measures.

Methods & coverage

Depth, physical access and asset quality

These are distinct WDI series, using the source definitions retained in the pack: FS.AST.PRVT.GD.ZS for private-sector credit, FB.CBK.BRCH.P5 for branches, FB.ATM.TOTL.P5 for ATMs and FB.AST.NPER.ZS for non-performing loans. No alternative credit series is substituted. Branches and ATMs are per 100,000 adults, not total population; NPLs are a share of gross loans. High credit depth is not automatically healthy lending, and low branch density does not establish shallow banking.

Mixed years and critically stale observations

Laos credit and broad money are only available for 2010 and are excluded from current rankings. Their original observations remain in the CSV, with historical context in country profiles. Singapore and Myanmar credit observations are 2020; Vietnam is 2022. Singapore NPLs are 2019 and Brunei NPLs 2022. Access-point years vary too: Brunei 2021, Myanmar 2020 and Timor-Leste 2023. Older rows retain year badges and Myanmar’s reporting caveat.

Gaps and related measures

Laos, Myanmar and Timor-Leste NPLs are intentional gaps. Broad money is available in the download as a separate monetary-depth measure, including its mixed vintages. No post-2010 Laos observation or external-balance series is invented.

Digital landscape / Findex → measures account ownership and use. Those demand-side indicators complement credit depth and physical infrastructure; they are not interchangeable and are not combined into one score.

Original gaps & vintage notes · Pack definitions · Research brief

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ASEAN in Data (2026). Credit & banking. /topics/credit-banking

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