ANTI-MONEY LAUNDERING (AML) - SANCTIONS & PEP SCREENING

ONBOARDING & ONGOING MONITORING

FinC.AML Name Screening

Fincom.co’s advanced “Phonetic Fingerprint” technology, will enable you to quickly trace any Entity by Name Pronunciation or “Name-Sound”, based on a Single Mathematical Representation. The resulted “Exact Name Matching” will improve and lower significantly your operational costs and prevent unnecessary fines, whilst increasing reliability and maintain your reputation.

ONBOARDING NAME SCREENING

Fincom.co’s advanced “Phonetic Fingerprint” technology, will enable you to quickly trace any Entity by Name Pronunciation or “Name-Sound”, based on a Single Mathematical Representation.

ONGOING MONITORING AUTOMATION (RPA)

On Fincom’s advanced platform, on any Alert verification, all Potential Flags, (similar phonetic Entities, other Entity Attributes) are saved in the systems’ database for Ongoing Verification, thus Eliminating the Risks of Whitelisting of Alerted Entities. This automation method leads to significant savings in the HR operational costs, as well as support rapid growth in volume of transactions / customers .

ENHANCING YOUR AML SOLUTIONS

Real Time

Automation

Protect

Safe

Cost Effective

Risk Reduction

From MANUAL to AUTOMATED Technologies

In non-automated systems, Alerts are resolved by the compliance team, and no further info is kept for future on-going verification. Moreover, once verified, past Alerted Names are forwarded to a White-listing, and thus have a major risk-exposure of missing real Alerts. Also – ongoing verifications are based on sample and random periodic batch files.

The Challenges of Name Screening:

  • Missed Hit (False Negative) – due to weak screening technology
  • Potential Alerts – Large number of Alerts in each periodic validation
  • Heavy Workload on Compliance team
  • Use of White-listing methods, results in significant Missed Hit
  • Periodic Validation – don’t inform on real time, and results in Missed Alerts in between validations

RESOLVING THE PROBLEMS OF WHITE LISTS

The complex task of screening the customer roster results in many false positives that re-appear and requires manual resolution, causing back log  and heavy workload that needs to be processed.

In trying to tackle this issue FIs have incorporated “White Listing” methodology which essentially after a customer was flagged and it was resolved in a positive manner, they will white list that customer so he will not re-appear; the problem is that  white listed customers can in time become sanctioned, simply due to more information surfacing that would identify them as sanctioned persons.

ADVANCED AUTOMATED TECHNOLOGIES

PHONETIC FINGERPRINT

Phonetic fingerprint is a mathematical representation of the pronunciation and phoneme of a name (individual person or other entity).

Using automated Real-time technology, supported by AI-ML layers, using over 48 mathematical algorithms, tracing phoneme & accurate name-matching across 38 different languages, transliterations and spelling variations.

ML TREND ANALYSIS  &  PERSISTENCE MECHANISM

An automated process, designed for ongoing verification resolving 60% of Potential Alerts, Automatically!

On each time there is a change in Sanction and PEP lists –  changes will be verified automatically, and only real-alerts will be forwarded for further resolution; so unless there is a change of data the Compliance team will not be Alerted for no reason, whilst if there is a change it will not be missed.

KEY FEATURES

ACCURATE NAME MATCHING

Indexing sanction lists/PEP/Blacklists using Phonetic Fingerprint technology. Names translated to singular numeric ID, enable rapid and accurate matching in real-time. The use of mathematical representations ensures high-accuracy for both FP reduction and avoid FN.

FILTER FALSE POSITIVES

applying AI/ML engines. Machine Learning looks at the frequency of Names, weighing and differentiating common names, unique names or combinations of names.

PREVENT MISSED-HITS (False Negative)

The use of Phonetic based methodologies (rather than rule based, enables Comparison of Entities in a very wide funnel; more name combinations, languages, transliterations.

ADJUSTABLE FILTERING THRESHOLDS

I) Phonetic Threshold – checks name composition; i.e. how many naming words are included in the entity-search; b) II) Name-Distance Threshold, resolve the difference between names that spelled or sounds alike.

AUTOMATIC RESOLUTION OF ALERTS

Based on Past Approvals Data, using ML Trend Analysis. automatic ongoing monitoring, resolution of re-alerts on the same entity, validates changes in watchlists on a daily basis, whilst reducing the workload on compliance teams

EFFECTIVENESS & EFFICIENCY

provide a wider name-screening coverage. Using advanced ‘name-understanding’, enable us to break thru the limiting traditional trade-off, that enforces compromising between FP and FN (as is in common fuzzy matching AI’s).

HOW DOES IT WORK

Pre-process: Indexing names in Sanction & PEP lists into “Phonetic Fingerprint” mathematical representation (Indexed Phonetic Mirror).

  1. Onboarding Customer information “Phonetic Fingerprints” of Entity is compared in wide funnel (multi-lingual) – to prevent Missed-Hits (False Negative). Whilst AI/ML engines are applied to filter out False Positives.
  2. Potential Alerts – are transferred upwards to the Pending DB which contains ‘flagged’ customers names.
  3. A resolution – by a manual verification. The name goes into the “Green All Clear Customer Database”.
  4. Ongoing Verification Process – Every Day entire customer roster is verified against Changes in Sanction & PEP lists, to ensure no customer is sanctioned.
  5. Results are pushed by a standard Rest-API into the user known UI/UX.

Additional Solutions from this category:

FinC.AML Level 1 Alert Suppression

Automation of Level 1/2 Alerts Resolution – Filtering AML L1/2 Alerts with automatic resolution of Potential Alerts

FinC.AML Level2 Alert Suppression

Automation of Level 1/2 Alerts Resolution – Filtering AML L1/2 Alerts with automatic resolution of Potential Alerts

FinC.AML Transaction Screening

Realtime Transaction Screening – Real-time check of ALL Entities involved in a transaction: Payer-Beneficiary-FIs

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