Basel AML Index methodology

Overview of methodology

The Basel AML Index uses a composite methodology, drawing its components from various publicly available sources. The Index was originally developed as follows:

  1. Extensive research was conducted in measuring money laundering and its challenges before the indicators were carefully selected.
  2. Only relevant indicators, sub-indicators and assessments that examine AML/CFT frameworks and other factors related to money laundering risk were considered.
  3. This was followed by the weighing of indicators according to their importance, based on expert opinions with academic, financial and senior AML experts.
  4. The methodology is reviewed every year. During the annual review meeting, external experts verify the quality of the data, country coverage and methodology. 
Independent researchConduct extensive research in measuring money laundering and its challengesBased on this research, create and refine a compo-ite methodology, i.e. scores calculated from a variety of public sources Several factors can lead to a high risk score in the Basel AML Index (see figure)Data selectionIdentify indicators that examine AML/CFT standards and related factorsReview the data and methodology of all potential indicatorsSelect only relevant and reliable indicators of ML/TF riskWeightingConvert 14 selected indicators into a 0 (low risk) to 10 (high risk) scaleAggregate indicators using a weighting scheme based on a qualitative expert assessmentVerify resultsAnnual review by international and independent panel of expertsStatistical testing using regression analysis and correlation analysisReview process ensures that the rating is accurate, plausible and continues to capture the latest developments in the area of ML/TF risks
AML Index methodology

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Factors that impact a country's risk score 

AMLindex_graphics_181003_04High riskHigh level of perceived corruptionLack of transparencyPoor financial standards and transparencyShortfalls in the AML/CFT frameworkWeak political rights and rule of law
Risk factors illustration

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Data sources

The Basel AML Index uses a composite methodology, meaning that it draws its components by aggregating publicly available third-party data sources.

The Basel AML Index aggregates 15 indicators and variables from relevant and reputable organisations in assessing the risk of money laundering/terrorism financing. Additionally, related aspects such as banking secrecy, level of corruption, financial regulations and judicial strength are factored in. Indicators may be added or removed by the Basel AML Index to reflect changing ML/TF risks and data sources.

Criteria for the inclusion of indicators

  • Relevance and relationship to risks of money laundering and terrorist financing (related survey questions or assessment of relevant financial standards and regulations)
  • Methodology of sources
  • Availability of recent data (maximum age of data is 2 years with the exception of FATF MERs)
  • Country coverage
  • Public availability
  • Low overlap with other indicators

The objective of the Basel AML Index is to provide a holistic picture of money laundering risk and therefore includes a wide range of indicators, each with a different focus and scope. A conceptual framework captures the multidimensionality of the data and categorises the indicators into five domains identified as key to ML/TF risks:

Domain 1: Quality of AML/CFT Framework (65%)

  • FATF Mutual Evaluation Reports (35%)
  • Tax Justice Network Financial Secrecy Index (20%)
  • US State Department International Narcotics Control Strategy Report (INCSR) (10%)

Domain 2: Bribery and Corruption (10%)

  • Transparency International Corruption Perceptions Index (5%)
  • TRACE Bribery Risk Matrix (5%)

Domain 3: Financial Transparency and Standards (15%)

  • Extent of Corporate Transparency Index (1.875%)
  • WEF Global Competitiveness Report – Strength of auditing and reporting standards (5.625%)
  • WEF Global Competitiveness Report – Regulation of securities exchanges (5.625%)
  • World Bank IDA Resource Allocation Index – Financial sector regulations (1.875%)

Domain 4: Public Transparency and Accountability (5%)

  • International IDEA Political Finance Database – Political disclosure (1.66%)
  • International Budget Partnership Open Budget Index – Budget transparency score (1.66%)
  • World Bank IDA Resource Allocation Index – Transparency, accountability and corruption in the public sector (1.66%)

Domain 5: Legal and Political Risk (5%)

  • Freedom House: Freedom in the World and Freedom and the Media (1.66%)
  • WEF Global Competitiveness Report – Institutional pillar (1.66%)
  • World Justice Project Rule of Law Index (1.66%)

Learn more about which indicators are used in the Basel AML Index and why.

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Scaling

Most indicators chosen for the Basel AML Index have their own scoring system. To achieve a unified coding system, individual indicator scores are collected and normalised using the min-max method into a 0–10 system, whereby 0 indicates the lowest risk level and 10 indicates the highest risk level.

All scores are scaled and standardised in this way in preparation for the next step of weighting each variable.

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Missing data

There is not always a complete set of 15 indicators available for all countries. Therefore, a country’s overall score is calculated based on available data only, and missing values are not replaced.

A country is only included in the Public Edition of the Basel AML Index if data on at least two key indicators from Domain 1 (Quality of AML/CFT Framework) are available.

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Weighting

In creating a composite Index, each variable receives a weight to aggregate all scores into one score. There are different techniques to determine the weight of each variable.

  • A standard, comparatively simple system consists of adding all the variables and weighting them equally. This assumes, however, that all variables are equally relevant in the context of ML/TF, which they are not.
  • Another method would be through statistical models, such as factor analysis and data envelopment analysis. Weights are in this case chosen to reflect the statistical quality of the data. Statistically, more reliable data with broader coverage are given more weight. The OECD Handbook on Constructing Composite Indicators states however that “this method could be biased towards the readily available indicators, penalising the information that is statistically more problematic to identify and measure." 
  • An alternative method is the expert weighting scheme or so-called participatory approach, where experts assign a weight for a variable based on their in-depth knowledge and expertise in the matter at stake.

After carefully assessing the advantages and disadvantages of each of these weighting methods, and given the specific AML focus, the Basel Institute has decided to use an expert weighting scheme to calculate the overall scores. The variables used differ in quality, coverage and relevance, with some components being more applicable than others in assessing ML/TF risk.

For example, country X may have a strong performance in rule of law, a low level of perception of corruption, but at the same time it could also have extreme loose regulations in financial transparency and weak standards to comply with AML/CFT obligation. As a consequence, country X may perform well under overall governance indicators but still could potentially be a high risk in terms of illicit assets and financial transactions.

The expert weighting system used places greater emphasis on indicators reflecting AML/CFT assessments and financial standards. The FATF Mutual Evaluation Reports, Financial Secrecy Index and US INCSR therefore have a significant effect on the country’s final risk score.

The expert weighting method includes a degree of subjectivity. The role of the annual Basel AML Index expert review meetings is critical in ensuring that the original weighting decisions continue to be adequate and are not influenced by bias or other undue types of subjectivity.

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Review meeting and changes in 2019

Each year the Basel Institute brings together external experts from a diverse set of AML, compliance and risk assessment backgrounds to review the methodology of the Basel AML Index for continued validity and adequacy, and to discuss trends in global AML regulation and practice that may impact its effectiveness. At the 2019 review meeting on 9 July, the following methodological changes were discussed:

New indicator: TRACE Bribery Risk Matrix

It was decided that the Basel AML Index would now include data from the TRACE Bribery Risk Matrix in Domain 2 (Corruption Risk). Even though statistical testing shows a high correlation between the TI CPI and the TRACE Bribery Risk Matrix, the addition of this new indicator adds a private sector angle to the corruption/bribery data coverage.

Up until July 2019, Domain 2 had a 10% weighting in the overall score and was solely covered by the TI CPI. The TRACE Bribery Risk Matrix and the TI CPI now receive an equal weighting of 5% each.

More research needed on trade-based money laundering

The expert group discussed the issue of trade-based money laundering and analysed several data sources to cover the issue. However, it was decided to continue research on possible indicators before measuring the risk for this issue and including it in the Index.

Case-based data on ML cannot be included

The expert group also discussed the feasibility of adding case-related data to the Index, for example, the Panama Papers, Paradise Papers and investigations by OCCRP that have revealed ML schemes involving a number of different jurisdictions covered by the Index. It was decided not to include case-based data due to the following reasons:

  • Time lags between real cases and detection. After a number of ML cases, some national regulators increased fines imposed on financial institutions for failing to enforce AML regulations. The resulting penalties were often imposed for transactions that happened many years previously. This huge time lag between real cases of AML misconduct and detection/sanctioning is problematic for an Index that is updated annually.
  • No regular updates. ML cases in the media appear without any predictable regularity. It is impossible to provide updates to the data and to change positions of countries.
  • Countries cannot demonstrate progress. Once a country is labelled a high-risk jurisdiction on the basis of its involvement in a ML media scandal, it is impossible for it to demonstrate progress in this area.

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Looking ahead

During development of the Basel AML Index 2019, it became clear that the quality of data concerning financial crimes is still a critical issue to be addressed. The FATF has taken positive steps by increasing the frequency of FATF updates and harmonising the methodology between different regional bodies. It is hoped that with a continuously increased frequency of fourth-round evaluations by FATF and its regional bodies, we can soon achieve full coverage and as such avoid skewed data due to outdated reports. The impact goes far beyond the Basel AML Index as the reports themselves, and the Basel AML Index, directly impact on the due diligence systems implemented by financial institutions and investors. 

Helping these institutions untangle the data and use it easily and more accurately in their own system is one of the aims of the recently upgraded FATF analysis provided as part of the Expert Edition Plus package.  

On the other hand, of course, it must be noted that, as illustrated by the latest high-profile ML cases, even low-risk countries are not entirely immune to money laundering risks or harm resulting from financial crimes. The resilience of their financial and public institutions is constantly tested by criminals. They need to stay on the radar for analysis for ML/TF risks. 

In this respect, opacity of beneficial ownership remains a key issue across all countries and requires a coordinated response at the international level. The Basel AML Index is seeking ways to improve data coverage for risks associated with non-transparent beneficial ownership. The same applies to risks associated with increasing volumes of international trade. Trade-based money laundering is getting more and more attention and we are looking at how best to reflect this and possibly other emerging trends in the 2020 edition of the Basel AML Index. 

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