Repeated investigations
A known typology is investigated fresh at every institution it touches.
Platform · Shared intelligence
Consortium KB, shared financial crime intelligence
See what other institutions already learned about a name or a pattern, without exposing your own customers, so you are not investigating a known actor from scratch.
0
Customer identities exposed
Consented
Contribution model
Real-time
Signal lookup
Shared
Typologies across members
Why this matters
Every institution learns the same lesson separately
A mule network hits one bank, gets caught, then moves to the next and repeats the whole cycle. Each institution investigates from zero because the intelligence never crosses the wall between them. Complead lets members share signals about patterns and actors, without ever sharing the underlying customer data.
No early warning
The first bank pays the cost, the rest get no signal until they are hit too.
Privacy blocks sharing
Institutions cannot legally pool raw customer data, so nothing is shared at all.
How it works
Scroll to advance
Four steps, sharing signals without sharing data
-
Step 01
Contribute a signal
When you confirm a typology or a bad actor, you contribute a privacy-preserving fingerprint, not the customer record.
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Step 02
Look up an entity
During a case, check whether other members have flagged the same pattern or fingerprint.
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Step 03
Read the signal
See the typology, how many members saw it and when, without seeing which members or whose customers.
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Step 04
Act in your own case
Attach the signal to your case as one input, weighed alongside your own evidence.
complead / contribute
- Contribution
- mulecluster
- fingerprintonly
- no PIIsent
complead / look-up
- Lookup0x9f21
- members flagged3
- type mulecluster
complead / read
- Signaldetail
- institutions3
- last seen2d
- confidencehigh
complead / act
- Added to case#7719
- consortiumsignal
- confidencehigh
Contribute
Look up
Read
Act
Capabilities
What the product actually does
Privacy-preserving fingerprints
Signals are shared as fingerprints, never raw customer data.
Consent-based contribution
You decide what patterns to contribute, under your own policy.
Typology library
Shared definitions of mule, layering and structuring patterns.
Signal lookup
Check any entity against member signals in real time.
Confidence scoring
Each signal carries a confidence based on member corroboration.
Full audit
Every lookup and contribution is logged for your records.
Product tour
Three screens your team lives in
Check before you investigate
Query an entity fingerprint and see whether the network already knows the pattern.
- Real-time signal match
- Confidence from member corroboration
- No member or customer identity revealed
complead / lookup
- 0x9f21
- members3
- mulecluster
- high
What the network learned, not who
The typology, the timing and the confidence, with your own exposure highlighted.
- Typology and category
- Last seen and member count
- Your linked customers flagged
complead / signal-detail
- mulecluster
- members3
- last seen2d
- of your customers linked1
What you share, on your terms
Review what your institution has contributed and adjust your sharing policy.
- Contribution log
- Policy controls per typology
- Withdraw a contribution any time
complead / contributions
- contributed42
- withdrawn3
- policy mule and layeringonly
What is shared
What crosses the wall, and what never does
Signals are fingerprints and typologies. Customer identities, balances and transactions never leave your environment.
Privacy model
Shared
Entity fingerprints, typology labels, timing and confidence
Never shared
Names, identifiers, balances, transactions
Consent
You choose what to contribute and can withdraw it
Audit
Every lookup and contribution logged
Runs on Fusion
Consortium signals sharpen the Fusion score
A consortium hit is one more input into the customer risk score, weighted by confidence, so a pattern seen across the network raises the score before it costs you.
This product
Consortium KB
Shared signals, typologies, lookups
Feeds into
Fusion engine
Network signal as a scoring input
You get
One risk score
Sharper on actors others have already seen
Integration
Look up a fingerprint, contribute a signal, over the API
Query the network during a case, and contribute confirmed typologies under your policy.
// look up an entity fingerprint
POST /v1/consortium/lookup "fingerprint": "0x9f21ab...", "context": "case_7719"
200 OK · 3 members · mule cluster · high
// contribute a confirmed typology
POST /v1/consortium/contributions "typology": "mule_cluster", "fingerprint": "0x9f21ab...", "consent": true
201 Created
FAQ
Before you ask us
Does our customer data leave our environment?
No. Only privacy-preserving fingerprints and typology labels are shared. Names, identifiers and transactions stay with you.
How does the privacy model hold up legally?
It is built so no raw personal data crosses institutions, with consent recorded per contribution. We will walk your DPO through the design.
What do we actually see on a lookup?
The typology, how many members corroborated it, when it was last seen and a confidence score, never which members or whose customers.
Can we stop contributing?
Yes. Contribution is per typology and can be withdrawn at any time.
How is a signal scored?
Confidence rises with member corroboration and recency.
Where is our data stored?
You choose the region: EU, UK or Türkiye.
Testimonials
All case studies
What compliance teams say
Complead provides ultimate control via its case management and safelist systems. Automated API operations significantly reduced my team's manual workload.
Using the API we run AML controls automatically and stay compliant, while reducing our team's daily workload.
With Complead we offer fast, easy, secure onboarding. We focus on real risks, not false positives.
See what the network already knows
In 30 minutes we will show how a signal moves across members without any customer data moving with it.
3,000+
Data sources checked
220+
Countries covered
15 min
Always real-time data