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Regulatory-grounded AI · Financial services
From complex rules
to clear action.
Regence AI automates risk-sensitive workflows using AI agents grounded in up-to-date regulations with human oversight.
Start with one workflow. Define the evidence. Measure the outcome.
REGENCE / ADVISER WORKSPACE● ILLUSTRATIVE
The question
Is this credit file ready for review?
A grounded review path
01
Check the evidence
Compare the file with the institution’s credit policy and required documents.
02
Surface the exception
Flag missing financial statements and show the supporting policy reference.
03
Keep the decision with your team
A reviewer assesses the evidence and records the next action.
Source-led analysisHuman review →
Designed for teams acrossBankingPayments & fintechRisk & complianceFinancial supervision
01 / Find your starting point
One platform.
Meaningful use cases.
Choose a focused workflow for your POC. We’ll agree the scope, data and success criteria before setting up your environment.
01 / USE CASE
Regulatory intelligence
Interpret changing requirements and connect questions to relevant source material.
Explore in a POC ↗02 / USE CASEAML & KYC
Explore onboarding reviews, risk indicators and exceptions that need human attention.
Explore in a POC ↗03 / USE CASECredit & fraud review
Surface missing evidence, review credit files and support structured investigations.
Explore in a POC ↗04 / USE CASETreasury & ALCO
Evaluate liquidity questions, policy constraints and decision-support workflows.
Explore in a POC ↗05 / USE CASEGovernance & reporting
Bring evidence, assessments and review actions together for governance teams.
Explore in a POC ↗06 / USE CASETrade finance
Explore document and policy review through the existing advisory experience.
Explore in a POC ↗02 / A clear path to your POC
Prove the value.
Before you scale.
Make success measurable
Identify your requirements, data constraints, reviewers and desired outcomes.
Agree the right POC
Your request goes to our team for review. Setup begins after approval, with any open questions resolved.
Test against your goals
Prepare the workspace and agreed data, validate access, and evaluate results with your team.
03 / Built around accountable decisions
AI assistance.
Human accountability.
A POC should demonstrate both useful outcomes and the controls your institution needs.
Grounded in evidence
Evaluate responses against relevant regulations, institutional policies and source references.
Designed for review
Identify exceptions, define accountable reviewers and agree what requires human approval.
Deployment that fits
Discuss data residency, private model access and integration needs before the environment is set up.
What could your team do better?
Bring a workflow. Let’s build a focused proof of concept.