For banks and other regulated industries
Enhance your regulatory mappings for AI automation.
Today’s mappings are built for people, not AI. R2 adds structure to your team’s existing taxonomy mappings, enabling AI to automate applicability and gap analysis.
MappingHow a regulatory mapping works
Mappings
Organized by internal categories
What banks track today
- Risk ratings
- Control owner
- Policy status
- Impacted LOBs
- Mapping notes
Not recorded
- Definitions
- Context
- Verified source links
Regulatory requirements
Needs
- Citations
- Obligations
- Summaries
Internal compliance processes and documents
- Policies
- Procedures
- Controls
- Tests
Links are maintained by hand. AI has to guess what each category means and why each link exists.
How R2 enhances
- Structures each source with precise citations
- Defines each category with your experts
- Verifies each link against the source
AI can assist with applicability assessment and gap analysis
01 — The problem with regulatory data
Can large language models (LLMs) process Regulatory Structure correctly?
Regulatory structures differ across jurisdictions, and inconsistencies in document structures wreak havoc on using AI for data conversion.
QuebecPrivacy act P-39.1p. 9
Structural issue
The page carries a second, invisible copy of the citation text.
What breaks
The citation resolves to a different section: s. 110 becomes s. 1101.
Expected structure
- 2021, c. 25
- s. 110
Broken structure
- 2021, c. 25
- s. 1101A different section
What they have in common
- The text, the page image, and the converter can each mislead.
- The deciding evidence can be invisible to extraction tools and AI models.
- Errors can be silent, and automated repair can make them worse.
02 — The problem with client data
Does your taxonomy increase or decrease errors when using AI?
We’ve found system of record (GRC) systems are out of sync:
- Fields set by a GRC vendor, often chosen a decade ago
- Shadow data kept outside the system
- Labels and mapping notes only your team can read
Tracked to manage risk
7 of 47 columns
- Inherent risk rating
- High
- Residual risk score
- 12
- Control owner
- AML Operations
- Policy status
- Active
- Impacted LOBs
- Retail, Commercial, Wealth
- Subtopics
- 30+ mixed labels
- Mapping info
- “See policy 4.2”
+ 40 more columns
None of these fields records what the category covers, what it excludes, or how it differs from similar categories.
Your team fills these gaps from experience. AI has to guess.
Illustrative record · Not client data
03 — How R2 works
Enhance the mappings you already have.
From 01
Regulatory requirements
- Problem
- Regulatory structures differ across jurisdictions.
- What R2 does
- Turns each source into structured requirements with precise citations.
- Result
- One consistent structure, linked to the text.
From 02
Internal categories
- Problem
- No field records what a category covers, excludes, or how it differs from similar categories.
- What R2 does
- Records what each category covers, excludes, and how it differs, with your experts.
- Result
- Each category carries the scope, inclusions, exclusions, and contrasts AI needs.
Result
Mappings
- Problem
- Links are maintained by hand, without a source passage or reason.
- What R2 does
- Checks each existing mapping against the source and recommends enhancements.
- Result
- Each link records its source and reason, for your approval.
Your team adds judgment.
AI adds scale.
Your experts keep responsibility for interpretation and approval.
Start with an assessment
Are your regulatory mappings ready for AI automation?
Start with one internal taxonomy. R2 shows where AI would have to guess during applicability and gap analysis, and which mappings to enhance first.
What you receive
- Findings on missing definitions and unsupported relationships
- Supporting citations for each finding
- Recommended mapping enhancements for your approval