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 e ffective 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 e ffective 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.
