BetaRisk and Governance squad

An AI compliance officer your MLRO can check

Zara Trustwell, Director AI Regulatory Compliance, an AI agent by FluxForceZara Trustwell — Director AI Regulatory Compliance

Zara Trustwell is an AI agent that maps your obligations by jurisdiction and turns written policy into controls your compliance team reviews and approves. She checks agent and analyst decisions against those controls and prepares regulatory reports as drafts for your MLRO to sign off. Your officers stay accountable.

Zara Trustwell
Zara Trustwell, Director AI Regulatory Compliance, an AI agent by FluxForce
Policy update: PEP review trigger changed
IllustrativeReady for review
Controls affected · 4
Flag explained
“New trigger changes two screening rules and one approval step.”
FATF R.12RBI KYC
REPORTS TO
Your MLRO or Head of Compliance
Shadow mode first
How Zara works with your team
Shadow mode
first: nothing acts until you say so
3 bands
of autonomy you configure
Every decision
has a replayable record
1 per agent
kill switch
SaaS · on-prem · hybrid
deployment
Product controls, not performance claims. Performance is measured on your data, in shadow mode.
The problem

The problem your compliance team faces every policy cycle

Your policy says one thing. Your screening rules, monitoring scenarios and analyst procedures were set up years ago and say another. Each new rule from a regulator means re-reading the policy, finding the affected controls and checking that teams actually changed them.

POLICY CYCLE
Weeks

to trace one policy change to every control

Regulators ask for the trace, not the policy.

Policy drift

Policy and practice stop matching

A threshold gets tuned, a scenario gets switched off, a review step gets skipped under pressure. The written policy never hears about it until an examiner compares the two.

Jurisdictions

One group, many rulebooks

A group operating in the UAE, India and the UK answers to different supervisors with different rules. The mapping usually lives in a spreadsheet that one person understands.

Reporting

Returns built by hand

Regulatory reports pull figures from screening, monitoring and case systems. Someone copies them into a template each period and hopes the definitions haven't changed.

Job description

What Zara Trustwell does Job description

Zara Trustwell is a Director AI Regulatory Compliance. She connects your obligations, your written policy and the controls that run in your systems, and shows your compliance team where they don't match.

AI AGENT · RISK AND GOVERNANCE SQUADBeta
Zara Trustwell, Director AI Regulatory Compliance, an AI agent by FluxForce
ZARA TRUSTWELL
Director AI Regulatory Compliance
REPORTS TO
Your MLRO or Head of Compliance
WORKS WITH
Your policy library, obligation register, screening, monitoring and case management systems
DEPLOYED
Shadow mode first, then the autonomy you set
KEY RESPONSIBILITIES
01Map obligations by jurisdiction to the policies and controls that meet them
02Turn written policy into draft controls for your compliance team to review and approve
03Check agent and analyst decisions against approved controls and flag exceptions
04Prepare regulatory reports and returns as drafts for MLRO sign-off
05Show which controls, rules and agent settings a policy change affects
AUTONOMY MODEL
Low risk
Can log a passed control check, if you allow it
LOW
Medium risk
Goes to your compliance team by default
MEDIUM
High risk
Always goes to your MLRO
HIGH
You set the threshold per rule.
Kill switch: Turn Zara off at any time
Shadow mode

What to measure in shadow mode on your own data

We don't publish performance numbers from our own tests. Run Zara Trustwell beside your current compliance process and measure what she finds in your policies, controls and decisions.

01
Obligations mapped
Share of your obligations linked to a policy and at least one working control.
02
Control drafts accepted
How many of Zara's draft controls your team approves without major rework.
03
Exceptions flagged
Decisions that broke an approved control, and whether your team agrees.
04
Missed exception review
Exceptions your own testing found that Zara didn't flag. Read this number first.
05
Policy-to-control gaps
Policy statements with no control behind them, found before an examiner does.
06
Time to report draft
Time from period end to a draft return your MLRO can review.
07
MLRO edits
How much your MLRO changes in each draft report, tracked over time.
08
Decisions with evidence
Share of control checks with a replayable record. The target is all of them.
Shadow mode results belong to you. We agree the metrics, the time window and who reviews Zara's findings before the trial starts.
How it works

How an AI compliance officer works with Zara Trustwell

Zara Trustwell connects to your policy library and decision systems through APIs. Your systems stay where they are.

01

Ingest

Zara reads your policies, procedures and obligation register, plus decision logs from your screening, monitoring and case systems and from other FluxForce agents.

02

Map

She links each obligation to the policy statement that covers it and the controls that put it into practice. Gaps and conflicts go to your compliance team as a list, with the source text attached.

03

Check

Approved controls become checks. Zara tests agent and analyst decisions against them and flags exceptions. Low-risk passes can be logged on their own if you allow it. Exceptions go to a person.

04

Draft

At period end she prepares regulatory reports and returns as drafts, with each figure traced to its source. Your MLRO reviews, edits and signs off. The draft, the edits and the approver go into tamper-evident evidence storage.

Want to see this on your data?

Run Zara Trustwell in shadow mode against one policy area and one jurisdiction. She maps, checks and drafts, and nothing changes in your live controls. Compare her findings with your last internal review.

Request a shadow mode trial
Compliance and regulatory mapping

Regulatory frameworks Zara Trustwell supports

Zara doesn't make you compliant. She keeps the link between each obligation, your policy and the control that meets it.

FATF Recommendations 10 and 12
Customer due diligence and PEP rules sit behind most AML policies. Zara maps them to the screening and review controls you run.
BSA and FinCEN CDD Rule
US institutions identify beneficial owners at 25% ownership plus one control person. Zara checks onboarding decisions against that control.
UK MLRs 2017 and POCA 2002
Suspicious activity reaches the UKFIU through your nominated officer. Zara drafts the supporting reports that officer signs.
PMLA and RBI Master Direction on KYC
Indian reporting entities map KYC and reporting duties to controls. Zara keeps that map current as directions are amended.
UAE federal AML law and goAML
CBUAE, DFSA and FSRA supervised firms report to the UAE FIU through goAML. Zara prepares report drafts for the MLRO to review.
AMLA and AMLC (Philippines)
Covered persons under RA 9160, as amended, report to the AMLC. Zara maps those duties alongside your other jurisdictions.
Analyst view

What your compliance team sees

One map from obligation to control. Exceptions arrive with the source text attached.

BEFORE ZARA TRUSTWELL
Obligations tracked in a spreadsheet
Policy changes traced to controls by hand
Control testing on a sample once a year
Reports assembled from several systems
Gaps found during the examination
AFTER ZARA TRUSTWELL
Obligations linked to policy and controls by jurisdiction
Affected controls listed when policy changes
Decisions checked against approved controls
Draft reports with each figure traced to its source
Gaps flagged to your team first
Options

How the options compare

CRITERIA Compliance consultantsGRC software Zara Trustwell, Director AI Regulatory Compliance, an AI agent by FluxForceZara Trustwell
Time to first results An engagement cycleConfiguration and data entry Shadow mode on one policy area
Who approves controls Your compliance teamYour compliance team Your compliance team, from Zara's drafts
Checking decisions against policy Sample-based reviewsManual attestation Checks decisions against approved controls
Keeping up with policy changes At the next engagementWhen someone updates the records Lists affected controls when policy changes
Report preparation Advice, rarely the reportTemplates you fill in Drafts for MLRO sign-off, figures traced to source
Where it's weaker Expensive and periodicTracks controls but doesn't read decisions Doesn't interpret new law for you. Your compliance team and counsel still decide what a rule means
Trust Builders

Built for Regulated Financial Institutions

01

Configurable autonomy

Low risk can run on its own if you allow it. Medium risk goes to a person by default. High risk always goes to a person. You set the bands per rule, channel and transaction type.

02

Kill switch

Turn Zara off without touching the other agents or your core systems. The switch, and who used it, is stamped on the record.

03

Shadow mode

Run Zara on live data with nothing blocked or closed. Compare the calls with your team's before anything changes.

04

Explainability

Every decision answers why, in plain English, with the signals and the rule or policy behind it.

05

Audit trail

Each decision is stored with its inputs, its reasoning and the person who approved it, in tamper-evident evidence storage.

06

No migration

Agents connect beside your systems through APIs. Your core banking, screening and case tools stay where they are.

Questions? We Have Answers

Frequently Asked Questions

FluxForce

Still have questions?

Talk to the people who build the agents. We'll answer per capability, yes or no.

It's an AI agent that does the mapping and checking work behind a compliance function: linking obligations to policy and controls, testing decisions against those controls and drafting reports. Zara Trustwell doesn't hold the officer role. Your MLRO or compliance officer stays named, accountable and in charge of every sign-off.

No. Zara prepares and checks. Your MLRO reviews her drafts, approves controls and signs every report. Exceptions on medium and high risk always go to a person. A kill switch turns Zara off without touching your other systems.

No. She prepares drafts with each figure traced to its source. A named person on your team reviews, signs off and submits through your usual channel, such as goAML, FINnet or the UKFIU portal.

Zara works with the obligations you load or approve for each jurisdiction where you operate. FluxForce focuses on India, the UAE, the Philippines, Kuwait, Jamaica, Mauritius, Kenya, the US and the UK. Your compliance team confirms how each obligation is read before it becomes a control.

Zara maps one policy area, checks decisions and drafts a report, but nothing changes in your live controls or filings. You compare her findings with your last internal review and decide what to switch on afterwards.

Your policies and procedures, an obligation register if you have one, and decision logs from screening, monitoring and case systems. Past audit findings and examiner letters help her find the gaps your supervisor already cares about.

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.

Shadow mode trial

See Zara on your data before anything changes

Run Zara Trustwell beside your current process. She works on your live data and records every call, and nothing is blocked, closed or sent until you decide.

  • Runs in shadow mode on your own data, next to your team
  • You agree the metrics, the time window and who reviews the results
  • Kill switch and a replayable record of every decision from day one
  • SaaS, on-premise or hybrid, with data residency agreed up front

Shadow mode results belong to you.

Take the first step

AI agents that prepare the case. Your team makes the call.

Start with one workflow in shadow mode, then decide how much each agent does on its own.

How we start
Discovery and scoping
Integration beside your systems
Shadow mode
Controlled autonomy
Govern and improve