Risk data aggregation reporting is a new regulatory requirement affecting global banking requiring banks to produce new and changeable reports on risk factors while demonstrating data governance.
Researchers Juan Sequeda and Mike Bennet [1] make the case that the most effective route to compliance is by using an ontology, such as one based on Financial Industry Business Ontology (FIBO). Reporting is enabled by semantic data virtualization technology, using W3C standards, such as R2RML, to translate queries from SPARQL into conventional database queries.
Semantic queries are used to recreate existing bank reporting templates and provide the means to extend and vary these quickly, with minimal application development. The non-disruptive nature of such an architecture means that existing bank systems of record are used, without the need for centralized data storage (relational or graph database) and existing data quality measures remain unaffected. They propose an initial probabilistic mapping method applied for each data source and queries may be built visually by business analysts,
Compliance regulations and associated policies govern a wide range of operations and activities that financial institutions, such as banks and investment firms, engage in every day. Compliance, and proof of compliance, are essential: for both external regulators and internal management. The complexity and amount of regulations and related policies are ever-growing.
In related research [2], the case is made for automating compliance decisions, suggesting that existing technology are either stand-alone or loosely connected components/services architectures: "Typically, these methods require humans in the loop at run time, often because the scope of automation only captures part of the substance of the relevant regulations and policies. Overall, compliance is best viewed as mostly an aspect of operations, whose automation should be woven tightly into operational systems as a whole rather than as a separate step that sits architecturally only loosely coupled to other operational activities."
Automated financial/regulatory compliance vendors tend to offer fragmented services, solutions and software "Even when these solutions are more, rather than less, complete for a particular regulation area, the solutions usually must accommodate extension to, and integration of, institution-specific policies, especially for larger institutional customers. In addition to utilizing vendors, larger institutions tend to implement quite a bit in-house as well. As with software applications generally, there is a momentum towards subscription and frequent updating, rather than traditional (infrequent-major-release) licensing, in both the software and the related data." say researchers Grosof et al, who propose an approach based on Textual Rules implemented in the Ergo platform which includes both a Reasoner and an integrated development environment (IDE), Studio.
Essentially, a rule based engine capable of making logical inferences (applying rules) to given data sets, providing very high logical expressiveness, including for higher-order, defeasibility, quantifiers, head disjunction, and meta knowledge, dynamic automated reasoning capabilities, and a detailed user-navigable explanations (for each answer) that are quite understandable by SME's.
The software integrates tightly some English natural language capabilities both for authoring (i.e., developing) rules - going from English phrases to logical expressions ("text interpretation") - and for generating answers with explanations - which leverages mapping from logical expressions to English phrases ("text generation"). Ergo also includes connectors that import knowledge from various encoded data and forms (e.g., relational databases and spreadsheets), then tightly integrate the imported knowledge into overall reasoning.
[1] http://fibo2016.dataversity.net/sessionPop.cfm?confid=101&proposalid=8885
[2]http://coherentknowledge.com/wp-content/uploads/2015/06/Grosof-etal-industry-track-paper-final.pdf
Researchers Juan Sequeda and Mike Bennet [1] make the case that the most effective route to compliance is by using an ontology, such as one based on Financial Industry Business Ontology (FIBO). Reporting is enabled by semantic data virtualization technology, using W3C standards, such as R2RML, to translate queries from SPARQL into conventional database queries.
Semantic queries are used to recreate existing bank reporting templates and provide the means to extend and vary these quickly, with minimal application development. The non-disruptive nature of such an architecture means that existing bank systems of record are used, without the need for centralized data storage (relational or graph database) and existing data quality measures remain unaffected. They propose an initial probabilistic mapping method applied for each data source and queries may be built visually by business analysts,
Compliance regulations and associated policies govern a wide range of operations and activities that financial institutions, such as banks and investment firms, engage in every day. Compliance, and proof of compliance, are essential: for both external regulators and internal management. The complexity and amount of regulations and related policies are ever-growing.
In related research [2], the case is made for automating compliance decisions, suggesting that existing technology are either stand-alone or loosely connected components/services architectures: "Typically, these methods require humans in the loop at run time, often because the scope of automation only captures part of the substance of the relevant regulations and policies. Overall, compliance is best viewed as mostly an aspect of operations, whose automation should be woven tightly into operational systems as a whole rather than as a separate step that sits architecturally only loosely coupled to other operational activities."
Automated financial/regulatory compliance vendors tend to offer fragmented services, solutions and software "Even when these solutions are more, rather than less, complete for a particular regulation area, the solutions usually must accommodate extension to, and integration of, institution-specific policies, especially for larger institutional customers. In addition to utilizing vendors, larger institutions tend to implement quite a bit in-house as well. As with software applications generally, there is a momentum towards subscription and frequent updating, rather than traditional (infrequent-major-release) licensing, in both the software and the related data." say researchers Grosof et al, who propose an approach based on Textual Rules implemented in the Ergo platform which includes both a Reasoner and an integrated development environment (IDE), Studio.
Essentially, a rule based engine capable of making logical inferences (applying rules) to given data sets, providing very high logical expressiveness, including for higher-order, defeasibility, quantifiers, head disjunction, and meta knowledge, dynamic automated reasoning capabilities, and a detailed user-navigable explanations (for each answer) that are quite understandable by SME's.
The software integrates tightly some English natural language capabilities both for authoring (i.e., developing) rules - going from English phrases to logical expressions ("text interpretation") - and for generating answers with explanations - which leverages mapping from logical expressions to English phrases ("text generation"). Ergo also includes connectors that import knowledge from various encoded data and forms (e.g., relational databases and spreadsheets), then tightly integrate the imported knowledge into overall reasoning.
[1] http://fibo2016.dataversity.net/sessionPop.cfm?confid=101&proposalid=8885
[2]http://coherentknowledge.com/wp-content/uploads/2015/06/Grosof-etal-industry-track-paper-final.pdf