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Product · Screening

Client Screening & Ongoing Monitoring

Screen every customer in real time. Catch every change within minutes.
Sanctions, PEP and watchlist screening at onboarding, continuous rescreening against data refreshed every 15 minutes, and one explainable risk score your regulator can follow & audit.

3,000+ Sanction, PEP and watchlists
220+ Countries in coverage
15 min List refresh interval
150 ms Median API response
Why this matters

You are not buying data. You are buying every decision made from it.

Every screening vendor sells the same thing: more lists, more sources, faster refreshes. But your team does not spend its day with data. It spends its day making decisions, and each one carries a name and a date. Fusion is built around the decision rather than the record, because a decision is the only part of screening a regulator can actually examine.

A decision made too late

The call came the day it was made. Then a list moved, and nothing told you. The file still reads clear, so nobody looks at it again.

A decision buried in a thousand others

When almost every alert is noise, attention becomes the scarce resource. The name that mattered was not missed through negligence. It was missed through volume.

A decision you cannot reconstruct

After a while, an auditor asks why. If the match logic, the list version, and the approver aren't attached to the decision itself, you can't demonstrate you're right.

How it works?

A few Easy Steps, then it runs automatically

Scroll to advance
  1. Step 01 Connect your customer base or do it manually

    One CSV, one API call, or a live sync from your core system. Entity resolution de-duplicates before anything is screened. You can also do this screening manually through our platform.

  2. Step 02 Scan as Real-Time at onboarding

    Fuzzy, phonetic, and transliteration-aware matching runs inside the API call. Choose your configuration, threshold, and profile.

  3. Step 03 Ongoing Monitoring on Every Changes

    Lists can change every minute; nobody knows when, but we can monitor our customers against to changes in real time and get updates immediately.

  4. Step 04 Manage the Case, Leave notes, Take actions and more

    If you get an alert, you have to manage it. With our ultimate case management features, you can easily make decisions, take notes, and take action.

complead / import
  • Records received1,284,902
  • entities resolved1,241,336
  • duplicates merged43,566
Sync healthy, last run 4 min ago
complead / screening / match
  • Aleksandr VolkovMatch on Sanctions
  • Profile matches2
  • Latin: 100%Aleksandr Volkov
  • Cyrillic 98%:Александр Волков
  • Arabic 94%:ألكسندر فولكوف
  • Chinese 88%: 亚历山大沃尔科夫
Fuzziness Rate: 85%
complead / monitoring / feed
  • Last Changetoday
  • new alerts7
  • Safelisted3
  • Auto-Resolved with Nova3
  • Remaining Alert1
NEW customer #8841 added to OFAC SDN (12:14, list update +6 min) → escalate. CHG PEP tier changed #4102 (11:48, tier 3 to tier 2). CLR delisted #2277 auto-cleared. Next sweep in 9 min
complead / cases / #8841
  • OPEN
  • sanctionsmatched
  • escalated toJohn Doe(MLRO)
  • MLRO review. 12:14 alert raised, OFAC SDN, score98
  • assigned to E. Yilmaz, analyst12:16
  • DOB and nationality confirmed against source12:31
  • Escalated to MLRO, SAR draft opened12:34
[File SAR/STR] [Export audit trail]
Automatic Data Flow
Real-Time Scanning
Ongoing Monitoring
Manage the Case
Capabilities

What the product actually does

01

Fuzzy and phonetic matching

Transliteration-aware across Latin, Cyrillic, Arabic, Greek and Chinese. Catches aliases, spelling variants and name order differences.

02

Secondary identifiers narrow the match

Date of birth, nationality, Gender, ID, and passport number are scored alongside the name, so a common name doesn't trigger a common alert.

03

One profile per person/company, not one per list

The same individual/company appears across dozens of sources under different spellings. Fusion resolves them into a single profile, so you review once, not the same person a dozen times.

04

PEP, Relatives and Close Associates

PEP exposure rarely sits in the PEP's own name. We screen and surface associate and family links, showing the relationship with our super-extended PEP data.

05

Thresholds by customer type/segment

Retail, corporate, and high-risk portfolios carry different risk appetites. Each has its own match threshold from different lists, and they follow different review paths, documented in policy rather than configured ad hoc.

06

Explainable scoring, Case Management and Full-Audit Trail

Every score shows which field matched, which list it came from, the source text and the threshold in force at the time. A cleared name stays cleared until the underlying record changes.

Product tour

Three screens your team lives in

One search box, every list

Type a name, pick which data types apply, and read the matches ranked by score. Every row carries its list origin.

  • Sanctions, PEP, watchlist in one query
  • Score band shown per result, not just pass or fail
  • Response returned in about 150 ms
complead / search-results
  • Volkov, Aleksandr ↵. Hits2
  • top score98
  • lists4
  • 148 ms. Александр Волков (OFAC SDN)98%
  • Aleksander Volkow (EU consolidated)74%

A queue that only holds real work

Alerts arrive grouped by customer, ranked by Fusion score, with the reason on the row. Auto-cleared deltas never reach the queue.

  • Ownership and SLA per alert
  • Bulk-clear whole match patterns once tuned
  • Escalation to MLRO in one action
complead / alert-queue
Level Ref Detail Score
High #8841 A. Volkov Sanctions 98
Med #4102 M. Aydin PEP tier 71
Med #7719 Delta Corp Adverse 66
Low #2277 J
Neves Delisted auto

The whole record, in one view

Aliases, dates of birth, roles, relatives and every source document behind the designation, with the original language text kept intact.

  • Source text and translation side by side
  • Relationship graph for connected parties
  • Exportable as an evidence pack
complead / profile-detail
  • AleksandrVolkov
  • RU
  • DOB1971-04-02
  • designated2022
  • SANCTIONED. Aliases: ВолковА
  • A.Volkow
  • Volkovs. Role: board member, state energy. Lists: OFACSDN
  • EU
  • UK HMT. Connected: 3entities
  • relatives2

Ongoing monitoring keeps screening active beyond onboarding.

Customers are automatically rescreened against sanctions, PEP and RCA, watchlist data based on their risk level. Lists update every 15 minutes. Any change in a risk profile raises an alert, and results land in case management.

Sanctions PEP & RCAs Watchlists Wanted Lists
Talk to an expert →
Monitoring · live in 9:00
scanning
Managed in case management 3 today
Data coverage

Coverage is the easy part. Proving what you knew is not.

Every vendor lists the same regimes. The question an examiner actually asks is narrower: what did your system hold on the day the decision was made, and why didn't it fire? Every record in Fusion is versioned and timestamped, so the answer is a query, not a reconstruction.

Every record carries what it said before, when it changed and which of your customers it touched. Delistings propagate the same way, so a cleared name does not keep firing.
Sanctions OFAC, EU, UN, HMT, DFAT, SECO and 200+ national regimes, alongside local and regional lists.
PEP Four tiers from heads of state to local officials, covering domestic, foreign, international organisation and RCA exposure, with position history on each profile.
Adverse media Interpol Red Notices, FBI Most Wanted, regulatory enforcement actions and disqualified director registers.
Freshness New designations reach your queue within the window. No overnight batch, no full re-run, no day you cannot account for.
Select the regimes and categories your licence requires, add your own blacklists and internal lists, and screen them together under one score.
AI Native Risk-Based Approach

Screening tells you about a name. Fusion tells you about the customer.

Client Screening returns a position on a name. Fusion turns that into a position on a customer: KYB adds the owners behind the entity, Transaction Monitoring & Fraud adds how they actually behave, and Adverse Media adds what is being reported about them. Multiple inputs, one unified score you can defend.

Customer Risk Assesment & KYC After you collect the data, the screening tool enters the game and helps to identify which parties are under blacklists
Feeds into With Transaction Monitoring & Fraud A change in payment behavior triggers an immediate rescreen and escalate the issue
This product KYB & UBO Verification Screens the beneficial owners your customer did not declare
You get With Adverse Media Advance Adverse Media performs borderline name matching against media resources; you can see a 360° risk appetite.
Integration

One call for the chain, a webhook for every change to it

Send the company, get the resolved structure with every party screened and the aggregate calculated. Subscribe to structural change and threshold events.

// screen a customer
POST /v1/screening "name": "Delta Corp Ltd", "identifiers": {"dob":"1974-03-12","nationality":"TR"}, "lists": ["sanctions","pep","watchlist"]
200 OK · 2 matches · 48 ms
// subscribe to ongoing monitoring
PUT /v1/screening/monitoring/8841 "events": ["match.new","match.cleared","record.changed","list.updated"], "url": "https://you.example/screening-events"
200 OK
Case study · Telecom

A national operator moved 1.2M subscribers onto continuous screening

Manual list checks ran monthly and covered one jurisdiction. After migration, the whole base is rescreened every 15 minutes across 220+ countries, and clearances carry a reason code.

−72% Manual review
1 day To integrate
1.2M Names in scope
Read the case study

See it screen your own names

Bring a sample file. In 30 minutes you will see the matches, the scores and the audit export they produce.

3,000+ Data sources checked
220+ Countries covered
15 min Always real-time data
FAQ

Questions screening teams ask us

What does the product screen itself against and what lies outside that screen?

The data it contains consists of sanctions and watchlists, PEP records relating to relatives and close associates, and wanted lists, all of which are obtained from more than 3,000 sources in over 220 countries. Two things are not included in it: adverse media screening and monitoring is a separate product that is incorporated into the same customer profile, and document and biometric identity verification is entirely outside the scope of Complead, being managed by your IDV provider.

What is the speed at which a screening call is made?

With a synchronous search the median time is 50 ms, which is fast enough to be sent as part of your onboarding request without the need for a loading screen. Bulk screening is carried out asynchronously and a report is provided for each batch.

Can we apply screening to the customers that we already have, rather than only those who are new?

Certainly, most teams begin by doing so. You can load the existing database using CSV, via an API or through a live sync with your main system. Entity resolution first removes duplicate entries, so even if the database contains 1.2 million records, it does not result in 1.2 million reviews.

How often are customers' status levels reviewed after they have been onboarded?

The lists are updated every 15 minutes and your base is continually checked against any changes. The rescreening depth is based on the customer's risk level rather than being carried out on a fixed monthly basis, so customers with a high risk level do not have to wait for the same schedule as those with a retail profile.

What occurs when a match is cancelled or a name is removed?

A name which has been cleared remains cleared until the original record is altered, so each time the list is updated the same false positive does not reappear. Likewise, delistings work in the same manner and end the alert by giving a reason code instead of leaving it open.

Is it possible for us to take our own lists with us?

Right, the internal blacklists, the lists provided by the local regulators and the customer-specific watchlists are just as much a part of the global data as the rest of it, are assessed using the same methodology, and are included in the same audit record.

What methods do you use to reduce false positives?

There are four points, and none of them involves a lower threshold; score bands for each customer segment, together with secondary identifiers such as date of birth and nationality, are scored along with the name, there is one profile per entity rather than one per list, and pattern level bulk clearing is carried out once a match pattern has been tuned.

Does the AI deal with alerts by itself?

It depends on how you define it. Once authorised, Nova auto deals with the deltas, for example in the case of a confirmed delisting or when a pattern which has already been cleared is repeated. In all other cases the matter is passed on to a person, and each automated action is logged together with the signals that led to it, so that the record indicates what decision was made and by whom.

What does an auditor see?

The name of the alert, who was responsible for it, their decision and when it was made, the list version that was in effect at that time, and the source text associated with the designation; the information can be exported in the form of an evidence pack either per case or per period.

What is the duration of the integration process, and is it possible to carry out preliminary testing?

Sandbox keys are handed out right away and the first live call takes place within 15 minutes. Production integrations, including the setup of monitoring webhooks, are usually completed in the same week. However, if your policy prohibits the sending of names, screening can be carried out using hashed identifiers. Information regarding data residency, retention and subprocessors can be found in the Trust Center.

Testimonials

What compliance teams say

All case studies
We went from checking customers one by one to a single platform that screens more than 3,000 of them around the clock, so our analysts can finally focus on real risk.
Ulviyya Akhundzada Head of Compliance & Monitoring · Ateshgah Life
We moved from screening customers one by one to a unified platform where our analysts can focus on what actually matters.
Mariana Alexei Non Banking Financial Expert · Moldcell
We focus on real risks, not false positives, meeting our AML obligations and our customers' expectations.
Arda Akay Head of Compliance, Risk & Internal Control · BPN