AML FULL-STACK
NAME-SCREENING SOLUTION

ACCURATE – EFFICIENT - EFFECTIVE

AML FULL-STACK
NAME-SCREENING SOLUTION

  • KYC / KYB Screening
  • Transaction Screening in Realtime
  • Ongoing Customer Roster AML Screening
  • Confirmation of Payee (COP , a.k.a BAV)

Onboarding – Quickly tracing any Entity by “Name-Sound”, based on a Single Mathematical Representation.

Ongoing – Potential Flags, (similar phonetic Entities or other Attributes) are saved in the systems’ database for Ongoing Verification, thus Eliminating the Risks of Whitelisting of Alerted Entities.

Transactions – Realtime Transaction Screening. Real-time check of ALL Entities involved in a transaction: Payer-Beneficiary-the 2 Banks.

COP/BAV – adding the powerful ability of ‘Accurate Name Matching’ across 38 languages, for confirmation that Beneficiary == Account owner.

MICRO SERVICES

FinC.AML Screening Assurance

a Plugin add-on to existing screening solutions; Multilingual Screening of unstructured Names or Entities.

Ensuring no False Negatives (Misses). Fincom.co proven technology, enhances the performance of all current AML Name Screening solutions, adding the unique ability in understanding over 38 different languages, and complex name structures. This service ensures highest level of compliance even with legacy solutions.

Advanced ML/AI Engines - easily integrated into the existing AML systems as well as a natural part within the work-flow process of the Compliance Team

FinC.AML L1 Alert/False Positive Suppression

Reduce the dependency on manual processes. Ensuring consistent outcomes, increasing efficiency, significant reduction in operational costs and risk of errors. Also – eliminating the inevitable inherent Discrimination by name alone, as is in legacy systems (example: Mohamad)

FinC.AML L2 Alert/False Positive Suppression

Encompass L2 team’s holistic investigation of the customer, transaction activity as well as other involved entities. Providing Entity Type match, Entity Enrichment screening by Attributes (ex. Address, BD, Geo) , ML Trend Analysis + Historical data, and AI Risk Scoring engine.

Micro Service – Big Optimization

Micro Service

– Big Optimization

The Challenge of AML Name Screening

COMMON MANUAL HR-BASED METHODOLOGIES

Banks are growing whilst being forced to meet the growing compliance regulation requirements; as a result, increase in compliance team size, workload and operational costs.

Alerts are resolved by the compliance team, taking time and effort. This huge investment is not  kept for future referencing and verification.

Even worst, past Alert Names are often forwarded to a Whitelist, exposing to major risks of missing real Alerts.

THE BAD PRACTICE OF WHITELISTS

The complex task of screening the customer roster, results in many False Positives that re-appear and requires manual alert-suppression, causing back log  and heavy workload requirements.

Many FIs have adopted the methodology of  “Whitelisting”, whilst ignoring the fact that  whitelisted customers can in time become sanctioned, as more information is surfacing (e.g., ING Bank – 700 Million €, EU fine for whitelisting, 2019)

RISKS & WEAKNESS

  • 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

A typical problem with Standard Sanction List Search

The CORE TECH
at the base of Fincom’s AML solutions

PHONETICS

– understanding the “sound”

COMPUTATIONAL LINGUISTICS

–  convert Sound into Number

DISTANCE ALGORITHMS

–  Increase Matching Accuracy

4 Propriety distance algorithms, 2 intra-language & 2 inter-language

ML  REGRESSION ANALYSIS

– persistence (using history of past decisions)

ML – FREQUENCY WEIGHING

 – frequency of similarities between names

AI  FUZZY LOGIC

– ensuring efficiency and effectiveness

AI SUGGESTION ENGINE

– Trend Analysis (combined with
ML-Persistence mechanism)

AI  UNDERSANTING NAMES

– Enhancing human Eye & logic

PHONETICS

– understanding the “sound”

COMPUTATIONAL LINGUISTICS

–  convert Sound into Number

DISTANCE ALGORITHMS

–  Increase Matching Accuracy

4 Propriety distance algorithms, 2 intra-language & 2 inter-language

ML - REGRESSION ANALYSIS

– persistence (using history of past decisions)

ML – FREQUENCY WEIGHING

 - frequency of similarities between names

AI FUZZY LOGIC

– ensuring efficiency and effectiveness

AI SUGGESTION ENGINE

- Trend Analysis (combined with
ML-Persistence mechanism)

AI UNDERSANTING NAMES

– Enhancing human Eye & logic

“Phonetic Fingerprint”
Understanding the “sound”

Matching between

 38 DIFFERENT LANGUAGES

&

HUNDRED SPELLING VARIATIONS 

Creating a common dominator matching numbers to numbers,

ensuring Accurate – Efficient- Effective results

MATCHING ENTRIES ACROSS MULTIPLE DATABASES
- The Technology behind

Using advanced phonetics, artificial intelligence (AI) & fuzzy logic in a multi-dimensional process:

SANITIZATION: ensuring the integrity of the information in the databases

NORMALIZATION: formatting & transforming data to a mathematical code   (phonetic fingerprint)

CALCULATION: matching the entries between databases by over 44 algorithms  accurately and efficiently.

PHONETIC FINGERPRINT
- Fincom.co patented technology

Phonetic fingerprint is a mathematical representation of the pronunciation and phonemes within the name (individual person or other entity). Using automated Real-time technology, supported by computational linguistics,  AI-ML layers, using over 48 mathematical algorithms, tracing the phoneme across 38 different languages, transliterations and spelling variations enables accurate name matching.

594360=Archibald=Archimbaud=Arquibaldo=Арчибальд

AUTOMATED TECHNOLOGIES
- Optimizing AML Operations

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.

L1 / L2 ALERT SUPPRESSION

An automated Micro-Service, designed to optimize Alert/False  Positive Suppression.

Enhanced AML operations with significant cost reduction.

FinC.AML

Advanced AI/ML Technology For AML Name Screening Solutions

FEATURES of FinC.AML NAME SCREENING

Transliterations, Spellings, Unstructured Names

provide a wider name-screening coverage. Using advanced phonetic based ‘name-understanding’, enabling seeing through different ways of writing names.

Accurate Name Matching

Indexing sanction lists/PEP/Blacklists based on advanced Phonetics enabling rapid and accurate matching. using unique mathematical representations to ensure high-accuracy for both FP reduction and avoid FN.

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.

Multi-Lingual

The ability to trace ad identify the same name across 38 languages and different pronunciations

Adjustable Filtering Thresholds

I) PHONETIC THRESHOLD – checks name composition; i.e., how many naming words are included in the entity-search; II) NAME-DISTANCE THRESHOLD, resolving the difference between names that spelled or sounds alike.

Filter False Positives

applying AI/ML technologies. Looking at the frequency of Names, weighing and differentiating common names, unique names or combinations of names.

EXPLAINABLE & TRACEABLE

AML Regulatory compliance demands: Explainable and Traceable screening solutions.

The Optimum system for that is based on pure mathematics – Objective, Traceable process, and completely Explainable. (in difference to rule-based empirical methodology -“what if”  which is Subjective).

Fincom.co uses Mathematics in Linguistics Computation and Phonetics formulae, in a way that is explainable and traceable. hence 100% Transparent supporting regulatory compliance auditing, management and adherence.

Enhancing AML Solutions

Consistent Outcomes

Elastic ‘Surge Capacity’

High-Efficiency

Realtime Transactions

Reduce Risk of Error

Significant Cost Reduction
(Operational savings)

Reduce Dependency
on Manual-Processes

Seamless Integration

WE DELIVER:

  • 500 verifications/Sec in Real-time
  • Reducing up to 90 % False Positives
  • Assuring No False Negatives
  • Prevent risk of ‘white list’
  • Low setup costs
  • Smart Automation reduce HR dependency

PHONETICS AT THE CORE

AVOID RACE DISCRIMINATION in NAMES SEARCH.

Example: US citizen named - Mohamad.

Simple database search will result with many people named Mohamad;
However, due to the inherent weakness in non-phonetic-based-technologies, it will result in hundreds of False Positives, that can be seen as discrimination and racism against Muslims, in which the name Mohamad is common.

FIND THE RIGHT ENTITY WITHOUT DISCRIMINATION

Therefore,  the Key-Challenge is searching in a WIDE-FUNNEL on the one hand, whilst being very ACCURATE in the name matching or entity matching.

Basing  this task on human-manual process alone will mean:

  • Will have to check each name by a team that covers many different languages
  • They will have to go through enormous amounts of data
  • Each name matching verification will take a relatively long-time
  • Bias based on race and nationality will become a major parameter

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