Privacy risk
Test data is still personal data
Masked copies of production data still carry real customers' behaviour. Moving them into test environments widens who can see them.
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Stella Simulant — Senior AI Staging & Simulation Lead
AI synthetic data analyst for testing fraud and AML rules
Stella Simulant — Senior AI Staging & Simulation LeadStella Simulant is an AI agent that generates synthetic transactions and fraud scenarios so you can test monitoring rules and models before they go live. She builds typologies such as mule networks and structuring, runs them through your rules, and shows what was caught and what slipped through. No production customer data needed.
Stella Simulant
Mule network scenario run
IllustrativeReady for review
Coverage gap · 2 patterns
Flag explained
“Two fan-out patterns passed current rules without an alert.”
Job description
What Stella Simulant does Job description
Stella Simulant is a Senior AI Staging & Simulation Lead. She builds synthetic transactions and fraud scenarios, runs them through your rules and models in staging and reports what they caught.
AI AGENT · THREAT SQUAD
STELLA SIMULANT
Senior AI Staging & Simulation Lead
REPORTS TO
Your Head of Fraud or model risk lead
WORKS WITH
Your staging environment, monitoring rules, fraud models and model risk inventory
DEPLOYED
Shadow mode first, then the autonomy you set
KEY RESPONSIBILITIES
01Generate synthetic customers and transactions that match the shape of your book, without copying real records
02Build fraud and money laundering scenarios such as mule networks, structuring and account takeover
03Run scenarios through rules and models in staging and record what was caught and what was missed
04Report coverage gaps to the rule or model owner with the scenario that exposed each one
05Keep a test record for each rule and model version, ready for model validation and examiners
AUTONOMY MODEL
LOWMEDIUMHIGH
Low risk
Can run scheduled regression scenarios on her own, if you allow it
Medium risk
New scenarios go to the rule or model owner by default
High risk
Coverage gaps always go to the rule or model owner
You set the threshold per rule.
Kill switch: Turn Stella off at any time
Options
How the options compare
| CRITERIA | Masked production data | Hand-built test cases | Stella Simulant |
|---|---|---|---|
| Real customer data in test | Yes, masked | No | No |
| Who decides | Rule owner | Rule owner | Rule or model owner, after reviewing Stella's results |
| Rare typologies | Only if they happened before | As many as someone writes | Built on demand from your chosen typologies |
| Volume and realism | Realistic, at production scale | Small and simple | Shaped on your book, at the volume you choose |
| Evidence for validators | Test notes | Test scripts | One test record per rule and model version |
| Where it's weaker | Privacy exposure and few fraud examples | Slow to build and easy to game | Synthetic data can miss behaviour only real customers show, so results still need checking against live outcomes |
Works best with
AI agents that work best with Stella Simulant
Stella tests before release. These agents use her results and watch what happens after.
RISK AND GOVERNANCE
Riya Intel
Director AI Governance & Model Risk
Adds Stella's test records to the model inventory and validation reports.
Meet RiyaTHREAT
Aiden Flux
Senior AI Fraud Risk Analyst
Has his scoring tested on Stella's scenarios before each retrained model is approved.
Meet AidenRISK AND GOVERNANCE
Zara Trustwell
Director AI Regulatory Compliance
Maps Stella's scenarios to the obligations and typologies each jurisdiction expects you to cover.
Meet Zara
Thoughts that matter
Insights on AI agents, financial crime and compliance
What we're learning about AML, fraud and the evidence examiners ask for.
Best AML Software in 2026: An Honest Comparison for Banks and Fintechs
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goAML Registration and STR Filing in the UAE: A Step-by-Step Guide for Reporting Entities
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Transaction Monitoring Software: UAE Acceptance Checklist
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PEP and Sanctions Screening: What the MLRO Owns
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Session Risk Scoring: Continuous Authentication for Banks
Read More
Impossible Travel Detection in Banking: How It Works
Read More
Questions? We Have Answers
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Frequently Asked Questions
Still have questions?
Talk to the people who build the agents. We'll answer per capability, yes or no.
Stella Simulant generates synthetic customers, transactions and fraud scenarios, runs them through your monitoring rules and models in staging, and reports what was caught and what was missed. Each run leaves a test record your model validators can review.
No. She reports coverage gaps to the rule or model owner, who decides what changes. She can run scheduled regression scenarios on her own only where you allow it. A kill switch turns Stella off without touching your other systems.
She learns the shape of your book from aggregate statistics you approve, such as products, channels and typical amounts. She doesn't copy individual customer records into staging.
Stella generates scenarios and runs them in your staging environment, and no rule or model changes. Your team compares her results with how you test today.
Common ones include mule networks, structuring, rapid movement of funds and account takeover. We agree the priority typologies for your book during scoping.
No. Synthetic scenarios test whether rules catch known patterns before release. Real outcomes from shadow mode and analyst decisions still show how rules behave on live customers.
FluxForce runs as SaaS, on-premise or hybrid, built on Microsoft Azure. We agree data residency and which components run inside your environment during deployment design, before any data moves.