Enhancing AML Compliance Through Data Quality Assurance

Ensuring adequate Anti-money laundering (AML) compliance requires dealing with significantly high volumes of data, which is generated by carrying out various AML compliance measures such as follows: 

• Customer Due Diligence (CDD) 

• • Know Your Customer (KYC)  

• • Sanctions Screening  

• • Politically Exposed Person (PEP) Screening  

• • Negative News Screening 

• • Customer Risk Assessment and Case Management  

• • Ongoing Monitoring of Business Relationship 

• Enhanced Due Diligence (EDD) 

• • Source of Funds (SoF)  

• • Source of Wealth (SoW)  

• Transaction Monitoring 

• Regulatory Reporting  

• • Suspicious Activity and Transactions Reports to Financial Intelligence Units (FIU) 

• Data of Correspondence with Customers. 

• Staff Training 

This infographic discusses how data quality assurance is an important requirement to strengthen AML compliance. 

All of these AML compliance measures generate data. This data is then further processed by AML software solutions for performing tasks such as screening, CDD, EDD, ongoing and transaction monitoring, regulatory reporting and facilitating communication with customers.  

However, the quality of output generated by AML software solutions heavily depends on the quality of data collected and disseminated. This makes it necessary for the Regulated Entity, such as a Designated Non-Financial Businesses and Professions (DNFBPs) and Virtual Asset Service Providers (VASPs) to ensure data quality assurance.  

The important components of Data Quality Assurance are as follows: 

• Data Governance 

• Data Cleansing 

• Data Validation 

Let us understand each of these components in depth: 

Data Governance: Refers to the process of ensuring the usability, integrity, and security of the data, which is collected from customers and disseminated, or in simple words, used by the Regulated Entity while carrying out AML compliance measures.  

Data governance can be ensured by DNFBPs and VASPs by ensuring that they have a robust and risk-based Data Protection and Data Privacy Policy in place. This process of having a written methodology for implementation is called data governance.  A data governance policy or data privacy or protection policy defines at an organisational level how the Regulated Entity intends to collect, store, use, and dispose of customer information and data by having in place proper protocols for the same. 

Data Cleansing: Refers to streamlining the data collected. Data, when requested from customers, is received in various formats and categories. However, when intending to input such customer data into an AML solution, the Regulated Entity needs to ensure that there is no duplication, replication, or inconsistency with acceptable and pre-determined formats of data that are supported by the AML solution they use. Data cleansing of old and obsolete data should, however, be carried out keeping in mind the regulatory record-keeping requirements. 

Data Validation: Refers to confirming that the data collected is verified across reliable and credible sources such as government records, publicly available information sources. Data validation helps in ensuring that the data collected to be utilised for AML purposes is accurate and reliable.  

Conclusion

Simply subscribing to AML software is an incomplete solution. Regulated Entities such as DNFBPs and VASPs must ensure that they have in place a robust and sufficient data governance policy that ensures data quality assurance by ensuring that the data is valid, clean, and adequately safeguarded according to applicable data privacy and data security laws and regulations. Ensuring data quality will go a long way in countering money laundering and terrorist financing, serve as strong evidence at the time of regulatory inspections and audits, and help entities meet mandatory record-keeping requirements.