Who are you really doing business with?

Entity Match Engine for Customer Onboarding. Challenges and facts from EU banking implementations. 


Customer onboarding is one of the most critical entry points of new data for a bank’s counterparty information. References and interactions with internal systems, externally sourced information, KYC processes, regulatory reporting and risk aggregation are all impacted by the quality of information that is fed into the organisation from the onboarding stage.

In times of automation and development of FinTech applications, the onboarding process remains largely manual. Organisations typically aim at minimising errors by means of human validation, often tasking an off-shore team to manually check and sign-off on the information for the new candidate counterparty. Professionals with experience in data management can relate to this equation: “manual data entry = mistakes”.

There are a variety of things that can go wrong when trying to add a new counterparty to an internal system. The first step is typically to ensure that a counterparty is not already present: onboarding officers rely on a mix of name & address information, vendor codes and open industry codes (e.g. the Legal Entity Identifier) to verify this. However, inaccurate search criteria, outdated or missing information in the internal systems and the lack of advanced search tools create the potential for problems in the process – an existing counterparty can easily get duplicated, when it should have been updated.

Datactics’ Entity Match Engine provides onboarding officers with the tools to avoid this scenario, both on Legal Entities’ and Individuals’ data. With advanced fuzzy logic and clustering of data from multiple internal and external sources, Entity Match Engine avoids the build-up of duplication caused by mistakes, mismatches or constraints of existing search technology in the onboarding process.

Another common issue caused by manual onboarding processes is the lack of standardisation in the entry data. This creates problems downstream, reducing the value that the data can bring to core banking activities, decision making and the capacity to aggregate data for regulatory reporting in a cost-effective way.

Entity Match Engine has pre-built connectivity into the most comprehensive open and proprietary sources of counterparty information, such as Bloomberg, Thomson Reuters, GLEIF, Open Corporates, Companies House, etc. These sources are pre-consolidated by the engine and are used to provide the onboarding officer with a standardised suggestion of what the counterparty information should look like, comprehensive of the most up-to-date industry and vendor identifiers.

“Measure and ensure data quality at source” is a best practice and increasingly a data management mantra. The use of additional technology in the onboarding phase is precisely intended as a control mechanism for one of the most error-prone sources of information for financial institutions.

Luca Rovesti is Presales R&D Manager at Datactics



Blog Categories: Featured, Finance, KYC, LEI, and Regulation.