Lower Cost, Greater Accuracy, Increased Efficiency
Fincom’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 “Accurate Name Matching” will improve and lower significantly your operational costs and prevent unnecessary fines, whilst increasing reliability and maintain your reputation.
The Trade off between Penalties vs. Poor Customer Service & Legal Claims. The Consequences are: damage to reputation, profit, share value and costs.
The Ideal Solution should include Reliable Identification, with no missed-hits, and no false positive.
Step 1- Indexing Sanction Lists/PEP/Blacklists with “Phonetic Fingerprints”
Step 2 – Comparing Entity’s “Phonetic Fingerprints” in wide funnel (multi-lingual) – covering 100% and avoiding Missed-Hits
Step 3 – Applying AI/ML engines to filter out False Positives
PHONETIC FINGERPRINT is a mathematical representation of the name of individual person or other entity (example: company name).
How do we do it?
Using automated Real-time technology, supported by AI-ML layers, using over 48 mathematical algorithms resulting in accurate matching of names across 38 different languages, transliterations and spelling variations.
TRACING PHONEME & ACCURATE NAMES MATCHING across –
Our range of AML compliance products, ensures high accuracy, helping you in meeting regulation requirements, for Onboarding Name Screening, Ongoing monitoring, using persistence automations, Realtime transaction screening, COP/BAV, Alert Suppression optimization engines, and more.
In Entity Resolution, our target is to identify all the data entries that are linked or referred to an Entity across multiple data sources as well as within the same database, creating a Single-Customer-View, the ‘Golden Records’ without compromising GDPR requirements, by using Fincom’s unique Anonymous search.
Data mapping, deduping, cleansing and unification, resolving the challenges of broken data, caused by multiple IT and data systems, different CRM, ERP’s, duplications of data-entries in and in-between databases, across multiple languages from different data sources.
A major challenge in AML Name Screening is numerous name variations that obscure the real entity. Sorting out different spellings, transliterations, and working and understanding
New US congressional legislation passed last month instructed FinCen of the Treasury Department to warn and make sure that financial institutions mainly banks stop the
Screening of Names is a fundamental action that financial institutions & Fintech’s must perform as part of their AML compliance obligations, and this seemingly simple
