One name, a dozen spellings
Arabic, Cyrillic and Devanagari names transliterate several ways. Fuzzy matching catches them all, and an analyst has to check date of birth, nationality and address for each one by hand.



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Aiden FluxSenior AI Fraud Risk AnalystFraud Detection & Risk Scoring
Rhea LedgerSenior AI KYC/AML Compliance DirectorKYC/AML & Sanctions Screening
Nova SentinelLead AI Zero Trust Security ArchitectZero Trust Access Security
Iris VermaAI Verification SpecialistIdentity Verification & KYC
Oscar GraySenior AI OSINT Intelligence DirectorOSINT & Threat Intelligence
Bella NovaAI BNPL Risk AnalystBNPL Risk Monitoring


28 specialized agentsAll systems operational
Ready to transform your security infrastructure?
Explore our complete agent library and request a custom demoView All Solutions


28 specialized agentsAll systems operational
Ready to transform your security infrastructure?
Explore our complete agent library and request a custom demoView All Solutions
Rhea Ledger — Senior AI KYC/AML Compliance DirectorRhea Ledger is an AI agent that screens customers against sanctions and PEP lists, resolves look-alike names across scripts, refreshes KYC when something actually changes and rates customer risk on your own factors. She drafts STR, SAR and CTR content for your MLRO to review. Your team signs off.

Screening tools return long lists of possible matches, and most are namesakes. KYC refresh runs on a calendar, so a customer whose ownership changed last week waits years for a review. Every disposition still needs a reason someone can defend.
make up most possible matches
Each one still needs a written reason.
Arabic, Cyrillic and Devanagari names transliterate several ways. Fuzzy matching catches them all, and an analyst has to check date of birth, nationality and address for each one by hand.
A new director, a new shareholder or a move to a higher-risk country changes a customer's risk today. A three-year review cycle finds it in year three.
Your MLRO pulls transactions, screening history and notes from separate systems before writing a narrative. The facts exist. Assembling them takes the day.
Rhea Ledger is a Senior AI KYC/AML Compliance Director. She sits beside your screening, onboarding and case management systems, works the match queue and prepares what your MLRO needs to sign off.

We don't publish match accuracy from our own tests. Run Rhea beside your current screening process and measure what she does on your customers and your lists.
Rhea Ledger connects beside your systems through APIs. Your screening lists and core stay where they are.
Customer records, ownership data, screening hits and transaction summaries arrive through an API from onboarding, core banking and your screening provider. List updates and customer changes arrive as events.
Rhea compares each possible match on name variants across scripts, date of birth, nationality, address and known associates. She scores the match and rates the customer on your own risk factors.
Your autonomy settings decide what happens next. Clear namesakes can close with a reason if you allow it. Medium risk goes to an analyst by default. High risk, including likely true sanctions matches, always goes to an analyst.
Where a case points to suspicion, Rhea drafts the STR, SAR or CTR content with the facts attached. Your MLRO edits and approves it. Every step, its inputs and the approver go into tamper-evident evidence storage.
Run Rhea Ledger in shadow mode beside your current screening process. She resolves matches, rates customers and drafts reports, and nothing is closed or sent. Compare her calls with your analysts' before you switch anything on.
Rhea doesn't make you compliant. She produces the evidence these frameworks expect you to keep.
Fewer namesakes to clear by hand. Each real match arrives with its evidence.
| CRITERIA | Manual match review | Screening tool only | Rhea Ledger |
|---|---|---|---|
| Time to first results | A hiring and training cycle | List setup and threshold tuning | Shadow mode on your live data |
| Who decides | Analyst | Match threshold, then analyst | Analyst, inside autonomy bands you set |
| Why a match was closed | Analyst notes, varies by person | Match score only | Plain-English reason with the fields compared |
| KYC refresh trigger | Calendar and ad hoc requests | Calendar | Events in the customer's data, plus your calendar |
| Report drafting | Written by hand | Not covered | Drafted for MLRO review and approval |
| Where it's weaker | Cost grows with match volume | High match noise across scripts | Only as good as your customer data and lists, and needs past dispositions before you trust the bands |
Rhea's files get more complete when other agents add what she can't see on her own.

Brings transaction behaviour and fraud scores into the customer risk picture.
Meet Aiden
Adds adverse media with a confidence level, with namesakes already filtered.
Meet Oscar
Confirms the person behind the record is real before screening starts.
Meet IrisLow 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.
Turn Rhea off without touching the other agents or your core systems. The switch, and who used it, is stamped on the record.
Run Rhea on live data with nothing blocked or closed. Compare the calls with your team's before anything changes.
Every decision answers why, in plain English, with the signals and the rule or policy behind it.
Each decision is stored with its inputs, its reasoning and the person who approved it, in tamper-evident evidence storage.
Agents connect beside your systems through APIs. Your core banking, screening and case tools stay where they are.
What we're learning about AML, fraud and the evidence examiners ask for.






Talk to the people who build the agents. We'll answer per capability, yes or no.
It works the screening and due diligence queue that analysts handle today. Rhea Ledger resolves sanctions and PEP matches, triggers KYC reviews when customer facts change, rates customer risk on your factors and drafts suspicious activity reports. Your analysts and MLRO review and approve the outcomes.
No. Rhea drafts the content with the supporting facts attached. Your MLRO or Principal Officer edits it, approves it and submits it through your usual channel, such as goAML, FINnet or FinCEN's system.
Only clear namesakes, only in the risk bands you allow, and each closure carries a recorded reason. Likely true sanctions matches always go to an analyst. A kill switch turns Rhea off without touching your other systems.
Rhea compares transliteration variants alongside date of birth, nationality, address and known associates. A name match on its own isn't enough to close or escalate. The reason she gives lists which fields agreed and which didn't.
No. She works with the lists and hits your current provider produces, and with your onboarding and core systems. You keep your lists and add Rhea's resolution and drafting on top.
Rhea works your live match queue and drafts reports, but nothing is closed or sent. Your analysts keep working as they do today, and you compare Rhea's calls with theirs. You decide whether, and where, to switch on any autonomy afterwards.
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.
Run Rhea Ledger beside your current process. She works on your live data and records every call, and nothing is blocked, closed or sent until you decide.
Shadow mode results belong to you.
Start with one workflow in shadow mode, then decide how much each agent does on its own.