AI-Driven Regulatory
Reporting Intelligence
A purpose-built regulatory intelligence layer that uses frontier language models, retrieval-augmented generation, and regulatory knowledge graphs to help global banks navigate reporting complexity across 11 jurisdictions and 200+ regulatory fields.
Why Regulatory Reporting Is Breaking Banks
Global banks face an unprecedented convergence of reporting demands. Multiple regimes, evolving schemas, conflicting data definitions, and accelerating regulatory timelines have pushed manual compliance processes beyond their limits.
Regulatory Fragmentation
A single derivatives desk may need to report under EU EMIR, UK EMIR, CFTC Parts 43/45, MiFIR RTS 22, and MAS regulations simultaneously — each with different field definitions, validation rules, XML schemas, and submission formats. EU EMIR alone mandates 203 reportable fields; UK EMIR requires 204. Reconciling overlapping but non-identical requirements across regimes consumes thousands of analyst hours annually.
Constant Regulatory Change
EMIR Refit went live April 2024. UK EMIR Refit followed in September 2024. CFTC rewrote its tech specifications in January 2024 with 49 further fields proposed. MiFIR RTS 22 revisions are expected in 2026. Basel III.1 implementation began January 2025. CSRD mandates sustainability reporting in waves through 2028. Tracking these changes manually across regulatory portals is operationally unsustainable.
Data Quality and Lineage Pressure
BCBS 239 principles on risk data aggregation demand that banks demonstrate complete data lineage from source system to regulatory report. Yet most institutions still operate with inconsistent identifiers (LEI, UTI, UPI), fragmented source systems, and manual reconciliation processes that introduce latency and error. Regulators are increasingly auditing data quality at the field level.
Manual Interpretation of Regulations
Regulatory technical standards (RTS/ITS), validation tables, XML/XSD schemas, reporting instructions, and regulator Q&A documents can run to thousands of pages per regime. ESMA’s EMIR validation table alone contains 200+ rules across multiple worksheets. Interpreting these documents, identifying changes between versions, and translating requirements into system specifications is a bottleneck in every regulatory programme.
The SustainsysAI Approach
Rather than replacing existing reporting infrastructure, SustainsysAI provides an intelligent regulatory knowledge layer that sits alongside bank systems — parsing regulatory source material, building structured knowledge representations, and delivering precise, citation-backed regulatory insights to reporting teams.
Frontier LLM Models
The platform leverages frontier language models to interpret regulatory documentation, extract structured requirements from unstructured text, and generate implementation guidance grounded in source material. Multi-model architecture routes queries to specialised models based on task type.
RAG + CAG Pipeline
Retrieval-Augmented Generation (RAG) and Context-Augmented Generation (CAG) ensure that every response is grounded in actual regulatory source documents. Vector embeddings of regulatory text enable semantic search across regimes, while context augmentation provides the model with precise, relevant passages.
Regulatory Knowledge Graph
Structured relationships between regimes, fields, validation rules, ISO standards, and reporting entities are maintained in a purpose-built knowledge graph. This enables cross-regime comparison, impact propagation analysis, and automated dependency mapping that would be impossible with flat document stores.
The REG AI Intelligence Stack
A four-layer architecture purpose-built for regulatory reporting intelligence. From trusted data foundations through knowledge graphs, frontier AI reasoning, to human-readable output — each layer is designed for traceability, auditability, and regulatory credibility.
The SustainsysAI Dashboard
A unified command centre for regulatory reporting intelligence — tracking regimes, fields, timelines, and implementation status across all active jurisdictions.
Regulatory Knowledge Graph
The platform ingests, parses, and structures regulatory source material from 40 official sources into a multi-regime knowledge graph — connecting fields, validation rules, schemas, and regulatory guidance into a queryable intelligence layer.
Source Material → Knowledge Graph Structure
INGESTION SOURCES
GRAPH ENTITIES
Structured Extraction
Validation tables, field specifications, and schema definitions are parsed into structured entities with typed relationships — not stored as flat text chunks.
Cross-Regime Linking
Fields that appear across regimes (e.g., UTI, LEI, Notional Amount) are linked at the canonical level, enabling comparison of definitions and validation rules across jurisdictions.
Version Tracking
Every document ingested is versioned. When ESMA updates its validation table, the graph captures what changed — new fields, modified rules, deprecated values — automatically.
RAG / CAG Architecture
Every regulatory insight generated by the platform is grounded in source material. The RAG/CAG pipeline ensures factual accuracy, provides citations back to official documents, and prevents model hallucination on regulatory content.
RAG Pipeline — Query to Cited Response
Retrieval-Augmented Generation retrieves relevant regulatory passages from the vector store before generating a response.
Context-Augmented Generation injects structured knowledge graph context (field metadata, validation rules, relationships) alongside passages.
Every response includes traceable citations to source documents, enabling regulatory audit trail compliance.
Document Chunking & Embedding
Regulatory documents are chunked using structure-aware splitting that respects section boundaries, table structures, and article numbering. Each chunk is embedded into a vector space using domain-tuned models, enabling semantic retrieval that understands regulatory terminology beyond keyword matching.
Multi-Model Routing
The architecture employs a multi-model strategy: specialised models handle schema parsing, validation rule extraction, and field comparison, while frontier models handle complex reasoning tasks such as cross-regime gap analysis and regulatory change impact assessment. Routing logic selects the appropriate model based on query classification.
Regulatory AI Agents for Each Regime
Regime-specific AI agents are pre-loaded with the regulatory knowledge base for their jurisdiction. Each agent understands the fields, validation rules, reporting formats, and regulatory history of its assigned regime — enabling precise, context-aware responses.
EMIR Agent
- 203 reportable fields
- ESMA validation table rules
- ISO 20022 XML schema
- Refit transition analysis
UK EMIR Agent
- 204 reportable fields
- FCA validation rules
- UK-specific deviations
- Transition period tracking
CFTC Agent
- 128 core + 49 proposed fields
- Parts 43/45 specifications
- Tech Spec v3.3 compliance
- Proposed rule tracking
MiFID Agent
- RTS 22 transaction reporting
- 65 current fields
- Revised spec (2026 expected)
- ESMA Q&A integration
SFTR Agent
- EU & UK SFTR coverage
- SFT lifecycle reporting
- Collateral and margin fields
- Trade repository integration
Basel Agent
- CRR3 / Basel III.1 tracking
- FRTB market risk reporting
- CVA risk framework
- PRA implementation mapping
APAC Agent
- MAS, HKMA/SFC, ASIC, JFSA
- CDE rewrite coverage
- Regional deviation mapping
- Phase rollout tracking
ESG / AML Agent
- CSRD reporting waves
- EU Taxonomy alignment
- AMLA requirements
- AML/KYC field mapping
Field-Level Impact Intelligence
The platform operates at the granularity of individual regulatory fields — the atomic unit of regulatory reporting. Every field is mapped, compared, validated, and scored across regimes to provide actionable implementation intelligence.
Canonical Field Mapping
58 canonical fields are maintained as a cross-regime harmonised data model. Each canonical field maps to its regime-specific variants, capturing differences in naming, data type, format, and validation requirements. This enables a single source system to serve multiple regulatory outputs.
Cross-Regime Comparison
For any given field (e.g., Unique Transaction Identifier), the platform compares the definition, validation logic, mandatoriness (M/C/O), data type, and ISO standard alignment across all active regimes. This identifies convergence points and regime-specific deviations that affect implementation.
Regulatory Impact Scoring
When a regulatory change is detected — a new field, modified validation rule, or schema update — the platform automatically scores the impact based on the number of affected regimes, downstream system dependencies, and implementation complexity. This feeds directly into the implementation backlog.
Example — Unique Transaction Identifier (UTI) across regimes
| Regime | Field Name | Data Type | ISO Standard | MCO |
|---|---|---|---|---|
| EU EMIR | Unique transaction identifier | AN-52 | ISO 23897 | M |
| UK EMIR | Unique transaction identifier | AN-52 | ISO 23897 | M |
| CFTC | Unique Swap Identifier (USI) | AN-72 | CFTC-specific | M |
| MAS | Unique transaction identifier | AN-52 | ISO 23897 | M |
Regulatory Change Monitoring
The platform continuously monitors 40 regulatory sources across 11 jurisdictions, detecting changes to validation tables, schema releases, consultation papers, and regulatory timelines — then automatically generating impact assessments and implementation backlog items.
What the Platform Monitors
Automated Change Detection
When ESMA publishes an updated validation table or the FCA releases revised reporting instructions, the platform detects the change, parses the differences, and generates a structured change report — within hours, not weeks.
What the Platform Generates
Impact Propagation
A single field change in one regime is automatically assessed for impact across the canonical model, downstream systems, and related regimes. Affected implementation items are flagged and prioritised.
Upcoming Regulatory Milestones
UK PRA Strong & Simple Framework Phase 1
PS6/24 implementation deadline for simplified prudential requirements.
MiFIR RTS 22 Revised Go-Live
Expected implementation of revised transaction reporting requirements under MiFIR.
EU AMLA Operational
Anti-Money Laundering Authority becomes operational under the EU AML Package.
Basel III.1 Full FRTB & CVA Mandatory (EU)
Full Fundamental Review of the Trading Book and CVA risk reporting requirements take effect.
CSRD Third Wave — Non-EU Companies
Non-EU companies with significant EU operations must report under CSRD.
Platform Architecture Overview
The platform is built as a layered architecture, from regulatory data sources at the base through parsing, knowledge representation, AI reasoning, and bank integration at the top. Each layer is independently scalable and auditable.
Integration with Bank AI Ecosystems
SustainsysAI is designed to operate within existing bank technology governance frameworks — integrating with internal LLM platforms, model risk management processes, and data governance structures rather than bypassing them.
Model Risk Management
All AI-generated regulatory insights include full provenance: the source documents consulted, the retrieval scores, and the generation parameters. This supports SR 11-7 model risk management requirements and internal validation processes. Models can be swapped or updated without changing the knowledge layer.
Internal LLM Integration
Banks running internal LLM platforms (Azure OpenAI, AWS Bedrock, on-premise deployments) can route SustainsysAI queries through their own infrastructure. The regulatory knowledge graph and RAG pipeline are decoupled from the model layer, enabling bring-your-own-model deployments.
Governance & Audit
Every interaction is logged with complete audit trails. Regulatory citations link directly to official source documents. The platform supports role-based access control, data residency requirements, and integration with bank SIEM systems for security monitoring.
Explainability
Every regulatory answer includes a citation chain: the question asked, the documents retrieved, the passages used for generation, and the confidence score. Bank compliance teams can verify any AI-generated guidance against the underlying regulatory source.
Supervisory Alignment
The architecture is designed to align with emerging supervisory expectations for AI in financial services — including ECB guidance on AI risk, PRA expectations on model governance, and MAS principles on responsible AI use in regulated activities.
Why Banks Need AI-Driven Regulatory Intelligence Now
The regulatory landscape is not simplifying. The volume of reporting requirements, the pace of regulatory change, and the supervisory expectation for data quality are all accelerating. Manual approaches have reached their operational limit.
Regime Convergence Creates Opportunity
The global shift to ISO 20022 XML, standardised identifiers (LEI, UTI, UPI), and harmonised CDE definitions means that for the first time, a single canonical data model can serve multiple regimes. But exploiting this convergence requires the ability to map fields across regimes at scale — precisely the capability that AI-driven knowledge graphs provide.
Regulatory Velocity Exceeds Manual Capacity
Between 2024 and 2028, banks face simultaneous implementation deadlines for EMIR Refit, CFTC rewrites, Basel III.1, CSRD, DORA, MiFIR revisions, APAC rewrites, and the EU AML package. Any institution attempting to track these changes manually across dozens of regulatory portals is accepting material operational risk.
Data Quality Is Now a Regulatory Priority
Regulators are increasingly scrutinising data quality at the field level. BCBS 239 compliance, DORA operational resilience requirements, and enhanced transaction reporting validation all demand that banks can demonstrate end-to-end data lineage from source system to regulatory submission. AI-driven field mapping provides this traceability.
Competitive Differentiation Through Intelligence
Banks that build regulatory intelligence capabilities now — understanding not just what to report, but how regulatory changes propagate through their systems — will have a structural advantage in implementation speed, compliance quality, and operational cost. Regulatory reporting is no longer a cost centre to minimise; it is an operational capability to optimise.
Ready to Transform Regulatory Reporting?
Discover how SustainsysAI can help your institution build an intelligent regulatory knowledge layer — reducing implementation risk, improving data quality, and accelerating compliance delivery.
across 3 continents
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Mildenhall, Suffolk · SustainSys Consulting Ltd · AI-Powered Engineering