Michael Atkin

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.

Financial Services Regulatory Issue Brief 

Leading analysts all share a similar view about global financial regulatory priorities. Complexity will  continue to increase with geopolitical events and regulatory fragmentation on the rise. The global economic environment will remain a key concern. There will be a push toward harmonized enforcement from financial crime and sanctions from war. And the new (and unique) risks from AI  will be a rising part of the regulatory agenda. This will translate into increased regulatory scrutiny with emphasis on enterprise resilience, risk management, cross-border data flow, low tolerance for poor governance and more prudential scrutiny.  

From a data perspective, these trends are driving the focus on data standards, granularity of reporting and interoperability across systems. Now is the time to rethink your approach to data management by putting your enterprise information into a knowledge graph where it is reusable,  traceable, accessible and flexible. We found in over 20 major financial services projects that this is both achievable and productive. You can’t be first, but you can be next ([email protected])  

Key Regulatory Initiatives  

Basel III

With endgame rules nearing finalization, financial institutions will need to step up preparations for the remaining Basel reforms as well as the long-term debt requirements. Variations in local approaches will add to the complexity.

T+1 Settlement

Compressing the settlement date is a response to the regulatory concern that “nothing good can happen between trade date and settlement”.This means pressure on accuracy and timeliness of data including links to legal entity relationships, risk metrics and trade corrections.

Books of Records

Data-centric architecture enables firms to place the client at the heart of operations. The key is the ability to rationalize data from multiple sources (i.e., IBOR, ABOR, PBOR, reference data) for advanced analytics and reporting.

Prudential Oversight

Banking authorities have an ambitious agenda including proposed changes to capital, resolution planning, solvency and supervision. These will require building effective control frameworks and prepare for new regulation on liquidity, capital requirements and stress testing.

FDTA Standards

The Financial Data Transparency Act is a new law designed to modernize the collection and sharing of financial data. The focus is on the adoption of machine-readable standards that are searchable without any loss of semantic meaning. FSOC are taking semantic data standards seriously. Joint rulemaking is forthcoming this September.

Data Implications

The most effective way of responding to these trends is to adopt data standards that were specifically
designed to address the challenges created by technology fragmentation. The goals are to ensure consistency, precision and granularity of data as it flows across processes and to promote flexibility in support of ad hoc (scenario-based) analysis.

These include the adoption of standard identifiers for all internal and special purpose IDs … the capture of precise and unambiguous meaning through well-engineered ontologies … and the expression of both identity and meaning in the language of the Web (i.e., IRI for identity, RDF for meaning and SHACL for business/logic rules).

1. Integration: The most foundational objective relates to harmonization of data across repositories. Organizing information using standards enables you to navigate across data sets and understand the web of relationships needed to identify risks, comply with new regulations and perform resiliency planning. 

2. Entity Resolution: By defining meaning via the ontology and linking it to the standard identifier,  you can track the origin, transformation and flow of data. This transparency into your data processes allows you to link glossaries, business rules and conceptual models to prove policy compliance to auditors.  

3. Data Quality: By organizing metadata and lineage into a structured graph, you gain a composite view of data assets to identify anomalies and deviations from expected data patterns and benchmark them against data quality rules.  

4. Compliance: By standardizing meaning and classifications you promote a shared understanding of regulatory requirements across stakeholders. This allows you to trace regulatory dependencies, ensure understanding of legal requirements and streamline regulatory reporting. 

5. Entitlements: By organizing users, roles and permissions in a knowledge graph, control officers can create granular access control policies that specify and automate who can access what resources under which conditions.

Semantic Arts  

Semantic Arts has been 100% focused on semantics, knowledge graph and ontology design for over two decades. It took hundreds of projects across dozens of industries for us to conclude that data-centric is the only reliable way to harmonize data across the enterprise.  

We have perfected a methodology that allows clients to migrate toward data-centric one sub-domain at a time. We start with a simple model of all the information managed by your line of business. This forms the scaffolding that is used to add additional projects on an incremental basis. We then work with you  to add capability to the knowledge graph architecture in terms of visualizations and natural language  search capability.  

Talk to us. We understand financial services and have a proven track record of helping financial clients including Broadridge, Capital One, Citi, Credit Suisse, Federal Reserve Bank NY, Freddie Mac, Goldman  Sachs, JP Morgan, Morgan Stanley, Nationwide, Sallie Mae and Wells Fargo.  

Note: The predictable response to this from some firms will be to double down on more expensive fire drills, war rooms and ad hoc solutions to these endemic problems. The alternative is to deal with the root cause — the massive fragmentation of your data landscape – rather than the symptoms. Take a page from the playbook of the digital natives and adopt data-centric knowledge graphs. 

Chemical Manufacturer: Faceted Taxonomies 

Capturing interrelations of information for relevance can be difficult, even with NLP. More often companies will seek to work in taxonomy space in their journey toward richer implementations of knowledge graphs for automation adoption. Our consulting services leveraged this approach to provide a foundation stepping stone as the company sought to bring inherent knowledge graph capabilities into their business. 

This global manufacturer had a sluggish system in place to comb through internet publications and look for key terms that might mark articles of interest to its divisions for competitive intelligence as a spawning point for innovative ideas. However, processes remained heavily manual and cumbersome. They realized that strong text matching and analysis was a missing component and decided to turn to taxonomies to mitigate and improve the process. 

Semantic Arts quickly discovered that the key to success was faceted taxonomies. We  worked with SMEs to determine what areas contained specific controlled vocabularies and  specialized terminology. As a starting point, Semantic Arts created a series of taxonomies  for each area for improved automation. Areas included: Products, Industries, Customers,  Capabilities, Manufacturers, Materials and Processes 

The tight focus of each facet allowed for SMEs and division experts to create very specific lists of terms. By using preferred labels and alternate labels (synonyms) for each, SA  enabled what could be recognized and matched in a desired internet corpus. Initial  implementation of the facets showed a higher level of matching to recognized terms of  interest than an NLP algorithm achieved, created a higher confidence in the significance of  the match, and left out many common or “stop” terms that the original method still picked  up. A start of efficiency was realized. 

Semantic Arts developed a more extended road map with the manufacturer to first refine  and bulk up the taxonomy lists based on continued implementation and analysis. By implementing, the client’s intent will be to apply a simple semantic layer to relate and interconnect the taxonomy facets. This ontology model will allow even richer inferencing  and matching of results based on relationships between terms (i.e., an article about a  specific product will imply the involvement of certain manufacturers even if they are not  explicitly mentioned). 

In this case, a small step in a focused step into taxonomy re-classification is helping to open more understanding about the broader benefit while allowing for faster delivery of more pinpointed research answers. In addition, building pipelines of connected unstructured information is consistent with organizational goals of harmonizing data for greater  strategic value. Divisions in other parts of the enterprise have taken notice and there is 

expressed interest in leveraging the unique re-usability and interoperability semantic capabilities enable after this initial pilot.

Morgan Stanley: Data-Centric Journey 

Morgan Stanley has been on the semantic/ data-centric journey with us for about 6 years.  Their approach is the adoption of an RDF graph and the development of a semantic knowledge base to help answer domain-specific questions, formulate classification recommendations and deliver quality search to their internal users. Their primary objective is to enable the firm to retrieve, retain and protect information (i.e., where the information resides, how long it must be maintained and what controls apply to it). 

The knowledge graph is being developed by the Information Management team under the direction of Nic Seyot (Managing Director and Head of Data & Analytics for Non-Financial  Risk). Nic is responsible for the development of the firm-wide ontology for trading surveillance, compliance, global financial crime and operational risk. Nic’s team is also helping other departments across the firm discover and embrace semantic data modeling for their own use cases.  

Morgan Stanley has tens of thousands of discrete repositories of information. There are many different groups with specialized knowledge about the primary objectives as well as many technical environments to deal with. Their motivating principle is to understand the  conceptual meaning of the information across these various departments and  environments so that they can answer compliance and risk questions.  

A good example is a query from a user about the location of sensitive information (with many conflicting classifications) and whether they are allowed to share it outside of the firm. The answer to this type of question involves knowledge of business continuity,  disaster recovery, emergency planning and many other areas of control. Their approach is to leverage semantic modeling, ontologies and knowledge graph to be able to comprehensively answer that question.  

To build the knowledge graph around these information repositories, they hired Semantic  Arts to create a core ontology around issues that are relevant to the entire firm – including personnel, geography, legal entities, records management, organization and a number of firm-wide taxonomies. Morgan Stanley is committed to open standards and W3C principles which they have combined with their internal standards around quality governance. They created a Semantic Modeling and Ontology Consortium to help govern and maintain that core ontology. Many divisions within the firm have joined the advisory board for the consortium and it is viewed as an excellent way of facilitating cooperation between divisions.

The adoption-based principle has been a success. They have standardized ETL and  virtualization to get information structured and into their knowledge graph. The key use  case is enterprise search to give departments the ability to search for their content by leveraging the tags, lists, categories and taxonomies they use as facets for content search.  One of the key benefits is an understanding of the network of concepts and terms as well as how they relate to one another within their organization. 

Semantic Arts ontologists helped engineer the network of concepts that are included into their semantic thesaurus as well as how they interconnect within the firm. They started out with over 6,500 policies and procedures as a curated corpus of knowledge of the firm.  They used natural language to extract the complexity of relationships out of their combined taxonomies (over half a million concepts). We worked with them to demonstrate the power of conceptual simplification. We helped them transform these complex relationships into broader, narrower and related properties which enable the users to ask business questions in their own context (and acronyms) to enhance the quality of search without manual curation. Our efforts helped reduce the noise, merge concepts with similar meaning and identify critical topics to support complex queries from users.

Investment Bank Case Study: Operational Risk 

In this major investment bank managing all the flavors of operational risk has become very balkanized. There are separate systems for process management, risk identification,  controls, vendor risks, cyber risks, outsourced risks, fraud, internal incidents, external incidents, business continuity, disaster recovery inter-affiliate risk and many more. 

To address, we were able to create an elegant ontology that captured all these aspects of  risk. We then (one-by-one) were able to extract and conform their existing information into this shared model. 

We managed to catch the re-write of a control library in mid-stream and get them to persist the key information directly to a triple store. The mappings have been ported into production, and we built (in TARQL) the capability to create a unified view of information systems that feed risk evaluation metrics. Additionally, an interactive graphics capability has been built directly on the triplestore for visualization across the risk portfolio.

Investment Bank: Resolution Planning 

This is one of the “too big to fail” banks, who are required by regulators to implement  “resolution planning” or as it’s known on the street a “living will.” The first few generations of the resolution plan were long on long textual descriptions of the nature of the interactions between various legal entities within the bank. 

Our sponsor recognized that the key to making a resolution plan workable is to make it data driven rather than document driven. Document-driven resolution plans are out of date as soon as they are written and require humans to read and interpret. While the firm,  as with most large financial services firms, consists of thousands of legal entities, there are  “only” a few dozen that are significant from a resolution standpoint. However, this is made more complex because hundreds of departments (may and do) have service relationships with their peers in other countries and time zones. Often these arrangements are tacit rather than spelled out, and even those that are written fall far short of the regulators desire to see specific mechanisms for controlling the work and assuring it gets completed. 

We based this project on the concept of Inter-affiliate Service Level Agreements. We designed an ontology of Service Level Agreements and in the course of four months iterated it through eight versions as we learned more and more about the specifics of getting a new system designed and built. 

In addition to (and in parallel with) the ontology development we built an operating system,  using our model driven development environment. We populated a triple store with data  sourced from many of their existing systems (HR for personnel and departments, finance for legal entities and jurisdictions, IT for applications, hosting and data centers and the activity taxonomy from the project we had performed the previous year). On top of this we built user interfaces that allowed managers to document the agreements that were in place between themselves and other departments in other legal entities. 

We completed the project in time to demo to the regulators and it is now being used as the basis for their go forward Resolution Plan.

Management Consulting: Enterprise Search 

This major consulting firm has the enviable problem of having every possible desirable expertise characteristic’s somewhere within their ranks of 300,000 employees, and the unenviable problem of trying to find those needles in such a gigantic haystack. 

It’s not that they are unaware of the problem. They have launched many projects over the years to address this, some of which cost hundreds of millions of dollars (and a problem easily worth this much to solve, but very small increases in chargeability or win rates on proposals as a result are worth that on an annual basis). 

We built an ontology to integrate projects and proposals around expertise and proficiency.  We have harvested as much as is known about current employees in terms of skills and proficiency (and we are beginning to get subcontractors and partners), but we know that this information is not being kept up to date. We are at the early stages of two more initiatives, one that will nudge people to update their profiles when it becomes known that there is demand in a particular area; the other, to combine externally available information with this primarily internally sourced graph. 

The other side of this project is to replace the game of telephone that is currently the primary way to find key people in the firm. Currently, senior staff or partners rely on their network to find experts. Junior people are more often left out. In either case the process is quite haphazard as each request gets forwarded on to another subset of the network.  There is a great deal of reluctance to “spam” their internal network, but there is also the need to find the right people as rapidly as possible. 

We have built an early prototype model with the vision that a chat-based service will leverage the graph network as well as keep track of the results, such that over time the requests will get smarter, smaller, and resolve faster.

LexisNexis: Enterprise Ontology 

We worked with this leading provider of legal and medical knowledge to build an enterprise  ontology for their wide-ranging content. In addition to building an ontology for their case law and statutory product lines, we worked with their Master Data Management Initiatives.  

They have over 30 MDMs in various stages of development with logical data models. These models (and therefore the MDMs themselves) were integrated manually, in a somewhat ad-hoc fashion. We built tooling to convert their existing logical models into a single integrated ontology, where the integration points were far more obvious. From there, we built tooling to convert the ontology back to a set of similar, but now conformed, logical data models.

With a successful 200-year track record in developing extensive and diverse product lines by leveraging chemistry and science, this global innovator was embarking on a new era of discovery to shape a better world. 

Categorization of products and the relationships between those named entities were difficult to describe in traditional taxonomies and “systems of systems”. Over the course of  2 centuries massive amounts of complex information had been arrogated, however it didn’t give a full picture. The patterns of data intersection for decision-making were lost within the siloed systems. For greater predictable business insights based not only on structured data but unstructured documents the need for easier access and interoperability was a primary goal. 

A CoE (Center of Excellence) was formed to accelerate R&D development, analytics and  finding of data to advance innovation with greater speed and reliability. A secondary mission was to socialize this capability to the broader community. Ontology development and tools to harmonize information were a foundational part of this data enrichment strategy, but in-house skills and data-centric modeling expertise were insufficient. It was necessary to develop as a core competency. 

Semantic Arts consultants were engaged to bridge this critical ontology and semantic capabilities gap. Our strategic advisory service offering brought over 25 years of practical implementation learnings and educational workshops to deliver a series of focused topics that met broad expectations of a teaching library. Recorded videos are now available on  the company’s enterprise intranet to traverse the complexities of information silos by enabling knowledge graphs, ontologies, and data-centric thinking. 

Topics include – Introducing Semantic Technologies and Ontologies, Introduction to OWL,  Introduction and Hands-on with Protégé and Property Rules, Understanding Class Relationships,  Expressions, and Property Restrictions, Semantic Triple Visualizations, Ontology vs. Taxonomy,  Topic Extraction and Weak Signals, and UI Visualization with Knowledge Graphs 

In parallel Semantic Arts are engaged with a division focused more on enriching and unifying 19 different data repositories into an ontology. The goal is to model relationships between the concepts, substances, and components for describing the products in a disambiguous manner to eliminate data duplication, increase metadata clarity, and eventually incorporate ML and NLP capabilities. We’re collaborating with the client to structure a roadmap for implementation. A projected iterative, agile approach by using our  predictable (rinse and repeat), Think Big / Start Small methodology will be employed to  guide and instruct in this digital evolution.

This effort to interconnect information assets for discovery complements and aligns with the broader digital transformational for bringing “miracles of science” to realization.

Semantic Arts worked with PwC to improve Verizon’s data privacy capabilities. 

Prior to the engagement, as applications evolved many of the process steps required to stay in compliance with privacy regulations and policies were performed manually using  multiple data sources. Our goal was to provide a knowledge graph as a single source of privacy metadata (information about data classified as private). 

Early in the engagement, we identified the key components of the data privacy landscape,  summarized in this diagram: 

The data set loaded into the knowledge graph identifies which applications and third parties are involved in each kind of privacy data processing: data collection, analysis,  modification, transfer, storage, etc. 

We modeled most of the privacy concerns implied by the diagram, including: legal rights of  a data subject, agreements with third parties, details of data processing relevant to privacy  concerns, data lineage, data retention, data catalogs, impact assessments, privacy-related  processes and tasks 

We also created an extensive taxonomy that allowed key distinctions to be made while the core model remained simple and straight-forward. Finally, we created generic configurable queries to perform a range of data validations. 

The result is a consolidated view of data from multiple sources that allows greatly improved management of application compliance with privacy data policies and regulations.