Semantics-based Enterprise Data Management Makes Know Your Customer (KYC) Compliance Easier

Today, the constantly evolving rules and regulations in every jurisdiction – including the US, the European Union, and the UK after Brexit – add to the exponential growth of data and overwhelm financial institutions with many data management challenges.

Banks need to compile, combine, and analyze ever-growing volumes of data in order to get to know their customers in the KYC process. They also need to make a thorough due diligence process in client onboarding in order to comply with all regulations in the relevant jurisdictions to avoid being inadvertently caught up in financial crimes or breach of sanctions, for example, which could have extremely negative effects on their reputation.

Financial Crime Compliance Risk Management

An April 2020 report from LexisNexis Risk Solutions found that financial services companies spend an estimated US$180.9 billion on financial crime compliance in the Asia Pacific (APAC) region, Europe, the Middle East and Africa (EMEA), Latin America including Mexico, and the United States and Canada.

LexisNexis Risk Solutions polled as many as 898 decision-makers who oversee financial crime compliance and compliance operations at their companies. According to the survey, financial firms in Europe for example now take longer to complete the due diligence in onboarding clients, which raises the total cost of financial crime compliance for the financial services firm. Moreover, in EMEA and LATAM, challenges in financial crime compliance have a negative impact on productivity at financial institutions, the survey showed.

“Financial institutions should investigate both the physical and digital identity attributes of their customers, leveraging data analytics to assess risks and behaviors in real time,” said Daniel Wager, vice president, global financial crime compliance strategy for LexisNexis Risk Solutions.

Knowledge Graphs for Efficient Compliance and Data Management

Constant regulatory changes in every jurisdiction and frustratingly tedious and resource-consuming onboarding and risk and regulatory compliance processes are some of the data management challenges to the financial services sector. Financial institutions need to analyze huge amounts of data from diverse data sources in various formats, thousands of pages of regulatory provisions, and a constant flow of news reports to comply with all rules and to get to know their customers.

This is where the knowledge graph – a machine-readable collection of interlinked descriptions of entities – comes into play to help the financial services sector efficiently integrate and analyze data. Thanks to the interlinked descriptions of every concept, person or event, and freely available interlinked facts from sources such as DBpedia, GeoNames, Wikidata, financial firms can now combine their proprietary data with external databases to gain more insights. The interlinked nature of the knowledge graph can trace complex relationship patterns of ‘who’s who’ to help firms in their KYC process. Combined with text analysis, the knowledge graph helps banks track news flows for accurate and efficient monitoring of news stories about their potential customers, thus avoiding a fallout in reputation.

This is why semantic technology provider, has created an advanced platform for organizing enterprise knowledge into knowledge graphs. Enabled by its powerful functionalities, companies in the financial services sector can obtain deeper insights from their data. In this way, they can reduce the time and costs for analyzing complex patterns when onboarding a client to comply with all regulations in all relevant jurisdictions.

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