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SANCTIONS & PEP SCREENING

ONBOARDING & ONGOING MONITORING

ONBOARDING NAME SCREENING

Fincom.co’s advanced “Phonetic Fingerprint” technology enables you to quickly trace any Entity by Name Pronunciation or “Name-Sound” based on a Single Mathematical Representation. The resulted “Accurate Name Matching” improves results, lowers operational costs, and prevents unnecessary fines whilst increasing reliability and maintaining your reputation.

ONGOING MONITORING AUTOMATION (RPA)

Fincom’s innovative platform allows for all Potential Flagged entries (similar phonetic Entities, other Entity Attributes) to be saved in the system’s database for Ongoing Verification, thus Eliminating the Risks of Whitelisting of Alerted Entities. This automation ensures greater precision and significant savings in the HR operational costs while supporting rapid growth of the volumes of transactions due to an improved throughput capacity.

ENHANCING AML SOLUTIONS

Real-time Results

Automation

Protection

Safety

Cost-Effectiveness

Risks Reduction

FROM MANUAL TO AUTOMATED NAME SCREENING

Manual Name Screening

In non-automated systems, alerts need to be resolved by compliance teams. No  information is kept for future ongoing verification.

Once verified, past Alerted Names are forwarded to White-listing, which results in a major risk of missing real Alerts.

Ongoing verification is based on sample random periodic batch files.

Challenges of Manual Name Screening

  • False Negatives (Missed Hits) – occur due to weak screening technology
  • Potential Alerts – large number of alerts in each periodic validation
  • Heavy workload – compliance teams spend long hours and days on manually filtering the alerts
  • Use of White-listing – results in significant amount of Missed Hits
  • Periodic Validation – lack of real-time information lead to Missed Alerts in-between validations

Current methods of resolving white-listing issues

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

In trying to tackle this issue FI’s have incorporated “White-Listing” methodology, meaning that after “flagged” customers are “cleared”, they are white-listed and taken out of the screening process. In case a white-listed customer is sanctioned after being white-listed, he or she will not be alerted and, eventually, flagged by the system.

 

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 40 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.

 

phonetic finger print

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

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).

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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