Our Data

Probability of Default (PD)

NUS-CRI Probability of Default (NUS-CRI PD) is a forward-looking point-in-time probability of default measure which is produced on a daily basis.

Key Facts

NUS-CRI PD covers over 90,000 exchange-listed firms worldwide and has a horizon ranging from 1 month to 5 years.
NUS-CRI PD is produced using a fully transparent and regularly updated methodology to provide a measure based on the latest research in credit risk and is trusted by organisations around the world for uses such as risk management, regulatory compliance and early-warning systems.

Sample Data

Company ID	1-month PD How likely is it to default in 1 month?	2-month PD How likely is it to default in 2 months?	...	60-month PD How likely is it to default in 60 months?
1	0.00012	0.00015	...	0.00030
...	...	...	...	...

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Use case	Frequency	Option
Non-commercial use, view on webpage/downloadable CSV files	Monthly	View Data
RESTful API integration and other commercial use	Up to daily	Contact Us

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Aggregate PD

NUS-CRI Aggregate PD allows you to assess the creditworthiness of any region, country or sector of interest, through a bottom-up (median) measure of credit risk.

Key Facts

NUS-CRI Aggregate PD covers countries across all major regions, including North America, Europe, Asia, and Latin America, and can be segmented by industry subcategories.

Sample Data

Country/Industry	1-month PD How likely is it to default in 1 month?	...	60-month PD How likely is it to default in 60 months?
US	0.00012	...	0.00030
US - Manufacturing	0.00012	...	0.00030
US - Manufacturing - Automobile	0.00012	...	0.00030
...	...	...	...

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Use case	Frequency	Option
Non-commercial use, view on webpage/downloadable CSV files	Monthly	View Data
RESTful API integration and other commercial use	Up to daily	Contact Us

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CRI MSME Credit Risk Data

~ Toolkit for MSME Risk Assessment & Credit Evaluation (TRACE) ~

Micro, Small, and Medium Enterprises (MSMEs) face a persistent financing gap because financial institutions are forced to choose between transparent but simplistic scorecards and high-performance but opaque 'Black Box' AI. The CRI Toolkit for MSME Risk Assessment & Credit Evaluation (TRACE) resolves this trade-off, extending our globally recognized methodology to the MSME sector.

Key Facts

TRACE is designed as a 'Glass Box', combining econometrically interpretable models with advanced machine learning. This delivers high-performance AI predictions while ensuring the auditability, stability, and supervisory comfort required by regulators and risk managers, finally reconciling performance with transparency.

Key Capabilities

Intelligent Data Integration: Fuses traditional financials with alternative signals (e.g., telecom, macro data) to overcome data sparsity.
'Glass Box' Modelling: Delivers high-performance AI prediction while ensuring full auditability and stability.
Regulatory Alignment: Deploys a GenAI-Driven Scenario Lab to stress-test models against 'edge cases' for responsible AI governance.
Proven Impact: Battle-tested across Southeast Asia to unlock MSME lending without compromising risk management.

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Use case	Availability	Option
Sample data for SG F&B companies	Yearly	View Data
Framework consultation and customization	On request	Contact Us

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TRACE White Paper

Bottom-up Default Analysis (BuDA) Toolkit

Jointly developed with the International Monetary Fund (IMF), the Bottom-up Default Analysis (BuDA) toolkit facilitates comprehensive credit stress testing and scenario analysis for portfolio risk assessment.

Key Facts

BuDA translates macroeconomic shocks into individual firm-level PD impacts, which are then aggregated into targeted economy, sector, or portfolio assessments using customizable stress scenarios.

Sample Stress Scenarios

Scenario	GDP Growth (1 quarter ahead)	GDP Growth (2 quarters ahead)	...	Fed Interest Rate (1 quarter ahead)	Fed Interest Rate (2 quarters ahead)	...
Baseline	2.5%	3.0%	...	-0.25%	-0.5%	...
Adverse	-1.5%	5.5%	...	0%	0%	...
Severe Adverse	-4.0%	7.0%	...	0.5%	0.5%	...

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Use case	Availability	Option
Toolkit implementation and training	On request	Contact Us

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BuDA White Paper

Real World Usecases

The items below are publicly verifiable citations showing our data used in research and publications by international organizations and regulators. Due to contractual and compliance confidentiality, we do not disclose the names of commercial financial institutions using our data.

Publications from International Monetary Fund that use our data

Corporate Quarterly Model. This paper details on IMF quarterly corporate risk model, which examines systemic non-financial corporate sector distress using firm-level probabilities of default (PD), covering 55 economies, and spanning the last three decades.
Chan-Lau, J.A. et al., 2017, Bottom-Up Default Analysis of Corporate Solvecy Risk: An Application to Latin America. IMF Working Paper 17/133.
Chan-Lau, J.A., 2021, Scenario Analysis with the DD-PD Mapping Approach: Stock Market Shocks and U.S. Corporate Default Risk. IMF Working Paper 2021/143.
IMF, 2021a, 2021 Financial Sector Assessment Program Review = Background Paper on Quantitative Analysis, IMF Policy Paper, Washington, D.C.
2021b, People’s Republic of China – Hong Kong Special Administrative Region, Financial Sector Assessment Program, Technical Note – Stress Testing the Banking Sector and Systemic Risk Analysis, June, Washington, D.C. Available at
2021c, How to Assess Country Risk: the Vulnerability Exercise Approach Using Machine Learning. Technical Notes and Manuals.
2020a, Republic of Korea, Financial Sector Assessment Program, Technical Note – Non-financial Balance Sheet Vulnerabilities and Risks to Financial Stability, September, Washington, D.C.
2020b, Regional Economic Outlook, October 2020, Middle East and Central Asia, Chapter 2.
2020c, Regional Economic Outlook, October 2020, Middle East and Central Asia. Technical Annex.
2019, Singapore, Financial Sector Assessment Program, Technical Note –Financial Stability Analysis and Stress Testing, July, Washington, D.C.
2017a, Japan, Financial Sector Assessment Program, Technical Note – Systemic Risk Analysis and Stress Testing the Financial Sector, September, Washington, D.C.
2017b, Indonesia, Financial System Stability Assessment – Press Release and Statement by the Executive Director for Indonesia, Washington, D.C.
2016a, Chile: 2016 Article IV Consultation – Press Release; Staff Report; and Statement by the Executive Director for Chile.
2016b, Canada: 2016 Article IV Consultation – Press Release; and Staff Report.
2016c, United Arab Emirates: Selected Issues.
2016d, Regional Economic Outlook, April 2016, Western Hemisphere, Chapter 3.
Iossifov, P.K., and T. Dutra-Schmidt, 2021, Cyclical Patterns of Systemic Risk Metrics: Cross-Country Analysis. IMF Working Paper 2021/028.
Loukoianova, E. et al., 2018, Managing Macro-Financial Linkages. Chapter 12 in L. Breuer, J. Guajardo, and T. Kinda, Realizing Indonesia’s Economic Potential. International Monetary Fund.
Miyajima, K., J. A.Chan-Lau, W. Miao, J. Shin, 2017, Assessing Corporate Vulnerabilities in Indonesia: a Bottom-Up Default Analysis. Asia-Pacific Financial Markets 24, 269–289.

Publications from Monetary Authority of Singapore that use our data

Moody's Analytics: MAS Report Reviews Stability of Financial Sector in Singapore, Dec 06, 2021
Macroeconomic Surveillance Dept. (2021). Financial Stability Review 2021 (No. 2021; Financial Stability Review, pp. 88–98). Monetary Authority of Singapore.
Macroeconomic Surveillance Dept. (2022). Financial Stability Review 2022 (No. 2022; Financial Stability Review, pp. 22–23). Monetary Authority of Singapore.

Publications from ASEAN+3 Macroeconomic Research Office that use our data

Ong, L. L., Siu, M. F. Y., & Laura Grace Gabriella. (2022). Capital Flow Management and Macroprudential Policy Measures in the ASEAN+3: Initial Recommendations ((PPP/22–01); Policy Position Paper). ASEAN+3 Macroeconomic Research Office.
The Winds of War: Spillovers from the Russia-Ukraine Conflict to ASEAN+3 Banking Systems (Analytical Note). (2022). ASEAN+3 Macroeconomic Research Office.
The Failure of Archegos Capital Management: Estimating Potential Spillovers to ASEAN+3 Financial Systems (Analytical Note). (2021). ASEAN+3 Macroeconomic Research Office.
AMRO Annual Consultation Report China—2020 (AMRO Annual Consultation Report). (2021). ASEAN+3 Macroeconomic Research Office.
Sun, W. (2020). Covid, Credit, and Contagion Risks to ASEAN+3 Financial Systems (Analytical Note). ASEAN+3 Macroeconomic Research Office.
- Slides: https://www.amro-asia.org/wp-content/uploads/2020/12/Covid-Credit-and-Contagion_4th-RFA-Research-Seminar.pdf
AMRO Annual Consultation Report China—2019 (AMRO Annual Consultation Report). (2020). ASEAN+3 Macroeconomic Research Office.

Publications from World Bank that use our data

Feyen, E., & Zuccardi, I. (2019). The Sovereign-Bank Nexus in EMDEs: What Is It, Is It Rising, and What Are the Policy Implications? [Working Paper]. World Bank.

Publications from European Systemic Risk Board that use our data

Zheng, H., & Schwenkler, G. (2020). The network of firms implied by the news (2586097211; Working Paper No. 108; ESRB Working Paper Series). European Systemic Risk Board; ABI/INFORM Collection; Publicly Available Content Database.

Publications from American Academy of Actuaries that use our data

The Probability of Default Model for Insurance Companies in the United States of America, Canada, and France (Full Text) American Academy of Actuaries, May 18, 2023.

Probability of Default (PD)

Key Facts

Sample Data

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Aggregate PD

Key Facts

Sample Data

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CRI MSME Credit Risk Data

~ Toolkit for MSME Risk Assessment & Credit Evaluation (TRACE) ~

Key Facts

Get Access

Bottom-up Default Analysis (BuDA) Toolkit

Key Facts

Sample Stress Scenarios

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