RISGRA® – is a “Symbolic” (foundational) hyper-graph model.

Symbolic models bring real-world “Judgement”, to counter-balance & complement the “Predictive” power of Neural AI and Agentic AI models (LLM + ML).

• Regulated organisations are grappling with the challenge of how to capitalise on the immense Predictive power of Neural AI and Agentic AI models, to create sustainable Enterprise Value;

• Concurrently there is a realisation that integration into operations of “black box” AI capability will fail to meet multiple regulatory hurdles, which require complex, non-linear decision processes to be explainable, repeatable and demonstrably fair to customers;

• Symbolic hyper-graph models (Ontologies) such as RISGRA® are foundational representations of real-world, curated knowledge that counter-balance and provide the guard rails that support the controlled implementation of Agentic AI;

• Organisations will need to rapidly develop bespoke, proprietary Neuro-Symbolic AI capabilities that are internally governed & controlled within the corporate perimeter;

• RISGRA® models are purpose-built to support this fast moving transition.

Integral to RISGRA® graph-schema are constructs that support the granular mapping (Bayesian Directed Acyclic Graph (DAG)) of risk transmission pathways, from: horizon risks; through crystallisation of risk incidents; to networked impacts; and extending to long-tail effects.

Many practitioners acknowledge that to-date, time as a dimension has been largely absent from graph languages and has been relatively unsophisticated in risk modelling more generally.

In collaboration with a niche graph partner spun out from the Alan Turing Institute, RISGRA® supports the integration of temporal data, enabling the modelling of: tipping points; cascade effects; temporary points of equilibrium and more.

At the heart of RISGRA® is a comprehensive schema (Ontology) - a coded database design that describes all RISGRA® concepts and constructs, and the many nuanced relationships between them.

Schema is “good medicine” in any database, it:

• ensures that the model is coherent and has design integrity

• provides guard rails that ensure quality, reliability and that outputs are explainable

• provides the foundation for inference and deductive reasoning, which unveils hidden risks and opportunities by linking seemingly unrelated data points.

RISGRA® scope includes both Ontology (for which read “Schema”, and for that please see Schema section above) and Taxonomy.

Taxonomy - RISGRA® includes a representative Risk Universe that comprises c.250 (and growing) distinct risk types, including classifications, definitions, and c.44,000 (theoretical maximum) causal inter-relationships (risk transmission pathways).

Building on the baseline risk taxonomy, multiple sector-specific variants of Risk Universe can be developed, for example an Aviation Risk Universe is currently under development.

Traditional databases are transactional, focusing on storing discrete data points. Knowledge “property” graphs rely on RDF triples (subject-predicate-object) to represent relationships between things.

RDF triples lack power and expressivity, often requiring “reification” - the act of artificially turning relationships (edges) into entities - in an attempt to represent more complex structures. This compromise undermines the integrity of the risk model and restricts its power and analytical depth.

RISGRA® capitalizes on a new, highly expressive hyper-graph language, that naturally supports modelling the complex and nuanced interfaces in the Enterprise Risk domain within a regulated organisation, and along it’s value chain.

Hyper-relations are integral to the design of RISGRA® - where Relations can participate in other Relations.

This enables the accurate modelling of:

• the complex, nuanced, multi-layered relationships & dependencies between risk management constructs;

• non-linear risk transmission pathways across the whole value-chain.

Many longer-established graph platforms not only persist with RDF triples at their core, but also carry legacy back-end technology and associated performance penalty.

RISGRA® partners with contemporary graph platforms that are optimised to ingest and analyse data orders-of-magnitude faster than the long-established graph products.

For example, throughput at 4.6 million updates a minute (c.77,000 per second), compare with averages closer to 6 updates per second for legacy platforms (=> Yes, that’s c.13,000x faster!)

This performance advantage is further underpinned by reduced time taken for messaging during analysis by over 10x.

Traditional database models, and many property graph models lack flexibility and are expensive to update.

In contrast, the design characteristics of the hyper-graph language used for RISGRA® ensure that making changes to RISGRA® schema is a relatively trivial exercise.

This a key strategic advantage when developing the model through successive releases, or when creating bespoke versions of RISGRA® for end-user organisations.