The German regulatory framework for combating terrorist financing (CTF) and money laundering (ML) is one of the most stringent in the world, designed to safeguard the integrity of the financial system. This framework is primarily governed by the Geldwäschegesetz (GwG), or German Money Laundering Act, which sets obligations for e.g. customer due diligence (CDD), risk management and risk analysis, transaction monitoring, transparency register, and reporting suspicious activities. These requirements are further strengthened by complementary regulations such as various interpretive guidelines issued by BaFin, Germany's financial supervisory authority. Additionally, guidelines from international regulatory authorities like EBA (e.g. the risk factor guidelines) and standard setters like FATF or Wolfsberg Group have to be taken into account.
The GwG and Kreditwesengesetz (KWG) mandate financial institutions to implement robust Anti Money Laundering (AML) systems capable of identifying and mitigating ML and TF risks effectively. This includes the establishment of systems to collect, verify, and monitor customer data throughout the customer lifecycle. BaFin’s AuAs BT KI3 guidelines underscore the necessity of integrating automated systems with accurate and transparent data flows to enhance risk management and ensure compliance with regulatory expectations.Although AML/CTF regulation often does not provide any clear requirements towards data quality, regulatory compliance obviously depends on maintaining data quality. Accurate customer records are essential for effective risk categorization, while real-time updates of customer data enable institutions to respond swiftly to emerging risks. In addition to accuracy and timeliness, the consistency of data across systems is crucial to ensure reliability and prevent discrepancies that could compromise AML efforts.
Before highlighting the importance of data quality in terms of stakeholder processes, the following subchapter provides a brief introduction to data quality.
What is data quality?
Data quality refers to the degree to which data meets the needs of its users. It describes the ability of data to provide reliable, relevant, and useful information for various purposes, whether for decision-making, regulatory compliance, or system operations. High data quality is essential to ensure efficient processes and the
desired value creation. Data quality is evaluated across several dimensions, which are often interconnected and mutually influential. […]
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Verfasst von
Christopher Hasenberg, Manager and AFC Compliance expert
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Dr. Ulrich Lechner, Senior Manager and AFC Compliance expert
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