Fincom.co's AML screening
FINCOM.CO
Description
Fincom.co’s Real-Time AML compliance solution uses purpose-built, supervised Machine Learning with “Phonetic Fingerprint” technology. It allows for screening high volumes of transactions at low latency, from multiple languages and industries, while dramatically reducing false positives without missing hits.
In a world where financial transactions are becoming immediate and demand real time verifications, manual processes under AML compliance consume enormous resources that result in extremely high operational costs. Moreover, US AML regulations are becoming even more strict: lower amounts, more and more industries must comply, fines are getting bigger and personal liability is now official. Fincom.co’s Real-Time AML compliance solution aims at addressing those 3 main challenges in ALM today.
Real-time low latency screening
AML verifications using rules-based/libraries solutions take between 3 seconds to 3 minutes on average. Fincom’s solution speeds up the process by evaluating the transactions against the largest Sanction/PeP databases, performing an accurate, precise screening under 200 milliseconds, while still ensuring "No Misses".
Greater accuracy while reducing false positives
Financial institutions hire thousands of trained expensive HR, costing up to 10% of the entire operational costs on financial crime only to screen false positives. By using a supervised machine learning technology, the false positive rate can be reduced by over 50% from the industry standard.
Multilingual “no miss” by Phonetic Fingerprint
Financial Institutions are fined due to outdated technology that can’t keep up with the diversity & volume of industries & languages. Using a mathematical representation of the way the spelled name sounds, this enables matching names at far greater accuracy, eliminating the problem of spelling variations with over 38 pre-built languages.
General information
How it works
Onboarding Customer
The customer information “Phonetic Fingerprints” of Entity is compared in wide funnel (multi-lingual) – Prevents Missed-Hits (False Negative), whilst AI/ML engines are applied to filter out False Positives.Potential Alerts
Alerts are transferred upwards to the Pending DB which contains ‘flagged’ customers names.Resolution
By manual verification, the name goes into the “Green All Clear Customer Database”.Ongoing Verification Process
Every day, the entire customer roster is verified against Changes in Sanction & PEP lists, to ensure no customer is sanctioned.Getting the results
Results are pushed by a standard Rest-API into the user known UI/UX.
How it looks
Welcome Screen
Real-Time matching of any Entity by Name Pronunciation or “Name-Sound”, based on Single Mathematical Representation. Show lessSearch result for queries
Using the most advanced solution for name matching to date, which incorporates Computational Linguistics. Show less
