Client 360 – A Foundational Challenge
When Lehman Brothers collapsed in 2008, CROs, CFOs and chief compliance officers were stuck pouring through annual reports and frantically searching within corporate documents to determine Lehman’s actual corporate structure – including who was bankrupt, who funded whom, who guaranteed what, and who would hold the obligations when everything was finally sorted out. It took an extraordinary 14 years after the collapse to find out due to the complexity of untangling a globally interconnected financial institution.
This legal entity identification problem during the 2008 financial crisis proved to be a systemic weakness that hampered the ability of regulators to understand and respond to what was happening in financial markets. Without a standard way of identifying financial institutions and their relationships to each other – it was near impossible to track interconnectedness, monitor risks, aggregate exposure, or coordinate regulatory responses.
KYC/AML
These challenges were not limited to systemic risk. Firms struggled to maintain consistent customer identification across their various lines of business and trading operations in order to meet their Know Your Customer (KYC) and Anti-Money Laundering (AML) obligations. The complexity of corporate structures makes it almost impossible to track fund flows, identify suspicious patterns or connect subsidiaries and affiliates across jurisdictions.
Client 360
The evolution from corporate entity identification to individual customer identification has been a natural progression in financial services as well as for many other industry sectors. The rise of the “customer 360” (better termed “client 360”) approach represents the goal of creating a complete and unified view of each customer across all business touchpoints. With fragmentation, however, an individual might have multiple accounts across different product lines using a host of name variations that result in missed opportunities for cross-selling or blindness in terms of relationship management.
“Customer” is a trigger word and has been one of the top data management challenges for companies since the beginning. The plethora of internal battles led to a simple conclusion – stop trying to harmonize the descriptors. There is no single view of customer. Every stakeholder’s definition is valid, just not the same. Focus on meaning, not words – make every person and organization the company touches an “entity” and assign to every entity a “role” (often multiple roles). Simple and elegant.
Root of the Problem
All three of these challenges – legal entity identification, KYC/AML and client 360 – stem from the same root problem: the inability to create consistent identity and meaning across various systems and databases. Each system speaks its own language and uses proprietary identifiers that become semantically incompatible data silos. Cross-border complications magnify the problem. These silos often number hundreds or thousands across large firms. Conventional approaches (deduplication, centralization, cross-referencing) have proven themselves to be unreliable.
This entity resolution challenge makes integration across sources extremely difficult and hampers understanding the relationship between clients, products, interactions, obligations and transactions. As a result, teams remodel the same entities in different systems. That makes it hard to reconcile. And within these divergent models, programmers use different terms for the same concept, use the same terms for different concepts or ignore important nuances altogether, making collaboration harder. These discrepancies and broken references are hard to detect across repositories. And while foreign keys and joins exist, they are often inconsistently modeled and poorly documented – requiring manual reconciliation by domain experts to find and fix data quality issues. The lack of entity (and meaning) resolution is risky, costly and totally unnecessary.
Semantic Standards as the Foundation
By addressing the challenges of entity and meaning resolution, organizations can aggregate all client data into a single, unified view. The most efficient and effective way to accomplish this is to put the data and the model at the center of the system. This is what we advocate as data-centric architecture – leveraging semantic standards and graph technology to ensure that applications conform to the data, not the other way around. Semantic interoperability is the key.
In a data-centric environment, we assign a unique identifier to every data concept. This enables firms to link data wherever it resides to one master ID – eliminating the need to continually move and map data across the enterprise. Rather than each system having its own definition of “customer,” “legal entity,” or “beneficial owner,” semantic standards ensure a shared understanding of requirements between business stakeholders and application developers.
As a result, systems can automatically understand and translate between different formats because everyone uses the same definitions for business concepts. When a new entity is created, the systems understand its place in the corporate hierarchy without additional mapping. Data and application models can be catalogued and mapped to ensure that users can find where the business concept resides. Instead of pulling data from multiple systems, data centric maintains a semantic model of each customer and their relationships. New data is automatically integrated based on semantic understanding rather than manual ELT processes. This data-centric approach becomes the foundational infrastructure for achieving Client 360.
Client 360 Maturity Cycle
Many in the financial services industry are already moving toward this vision, with leading institutions implementing knowledge graph solutions built on semantic standards. The migration from solving entity identification problems to enabling Client 360 represents more than technological evolution – it’s a fundamental shift toward semantic-first data architecture (without the rip and replace of traditional methods). Below is a three-level maturity guideline to help you implement a common language across your organization …
1. Maturity Level 1: Demonstration of Capability – This involves working with your SMEs to verify business requirements and build the business capability model. This goal of this maturity level will be to integrate at least two of your client-related datasets into a single model based on the client 360 ontology. The team will write and execute scripts to transform the data and test it for logic and reasoning validity. The result will be a core knowledge graph to enable key stakeholders to understand the query and analytical capabilities of data-centric by looking at their own data.
2. Maturity Level 2: Expanded Capability – This level focuses on harvesting additional customer datasets related to client 360. The goal is to link use cases (i.e. KYC, risk exposure analysis, CCAR, Basel III, FRTB, BCBS 239, Rule 4210, lineage traceability, cost of service, customer classification, profitability analysis, issue management, etc.) based on your internal priorities. This result will be an expanded domain ontology required to implement entity resolution and ensure conformance of the data to internal data service (DSAs) and service level agreements (SLAs).
3. Maturity Level 3 – Semantic Operations and GUI – Install a licensed, production-ready triplestore. You will rewrite RDF transformation scripts for your internal environment and set up data transformation workflows. This includes implementing change management approval processes, automated quality testing and entitlement controls. This should include training in expanding analytical and reporting capabilities as well as implementation of graphical user interfaces.
A Final Word
Starting your data-centric journey with legal entities and individuals is strategically sound as well as wise data policy. These “entities” represent the core actors in almost every business process. They are the primary subjects that most other data points relate to – they drive transactions, sign contracts, participate in supply chains and have a wide variety of relationships with your organization. We have learned that by adopting semantic standards for these foundational elements, you create a stable baseline upon which all other data relationships can be built.
By virtue of their centricity, restructuring your data environment as a connected infrastructure for organizations and people delivers immediate and tangible value across most departments and in terms of relationship management, reduced data duplication and enhanced regulatory compliance. Adopting data-centric standards for clients and legal entities is the first step in unraveling the critical connections that translate into better risk assessments and opportunity identification. Client 360 represents the path of least resistance – and one that delivers maximum initial impact for your organization.
