Incomplete or outdated KYC data sets are a common finding in regulated companies — whether as a result of supervisory audits, the merging of heterogeneous system landscapes, or simply due to structures that have grown over many years. With the AMLR and increasingly stringent requirements for data quality, timeliness, and traceability, the pressure to act will continue to rise. We support you in cleansing, structurally consolidating, and establishing your KYC data sets on a foundation that sustainably meets supervisory expectations.
About
KYC data is the raw material of any effective anti-money laundering system. Its quality determines whether risk classifications are sustainable, whether monitoring systems deliver reliable matches and whether customer relationships can be documented in a compliant manner. In practice, companies face two closely related challenges: cleansing existing databases and building a structurally sustainable data architecture for the future.
We support both areas of responsibility: from the targeted remediation of individual portfolio segments to the conceptual realignment of KYC data management. In doing so, we combine regulatory expertise with technical understanding and operational implementation experience.
Our services include:
KYC Remediation and Data Cleansing — identification of data gaps and inconsistencies, risk-based prioritization of portfolios, subsequent collection of missing information and documentation of the measures implemented
KYC Refreshes and Look-back Analyses — systematic updating of customer data according to the respective risk class, including the processing of historical portfolios following audit findings
Conception of Structured KYC Data Warehouses — development of data models, definition of binding data fields and mandatory information, specification of update cycles and responsibilities along the customer lifecycle
Interface and System Design — analysis of existing source systems, definition of data flows between KYC, onboarding, monitoring and core banking systems, as well as support in the selection and implementation of suitable tools
Governance and Quality Assurance Structures — establishment of permanent control mechanisms, data quality metrics and clear responsibilities so that achieved data quality does not erode again
Upon request, we can handle the operational implementation with our own resources or provide conceptual support and manage the measures together with your internal teams.
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