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Fintech

Anti-Money Laundering Use Case

The problem

Every year, about 2%-5% of the world’s GDP is involved in money laundering activities.

This is equivalent to almost 2.5 trillion dollars per year destined to fraudulent actions.

The solution

Anti-Money Laundering (AML) identifies the existence, nature, source, ownership, location, and disposition of property derived from fraudulent transactions.

When the system identifies a fraudulent transaction, it flags it for expert analysis.

The technology

AML uses Fuzzy Match Levenshtein distance, a string metric to detect the difference between two sequences.

Current systems are limited to time-bound checks against 10,000-entry lists published by governments or specialized firms.

In a fraction of a second, accelerated AML can check 6M-entry lists while current systems analyze 10,000 entries only. 2 All this happens in real time, when the transactions are taking place. 3 When the system identifies a fraudulent transaction, it flags it for expert analysis. 4 1 Anti-Money Laundering (AML) identifies fraudulenttransactions by looking tofind similarities in operationsagainst the Deny listestablished by thegovernments or regulators.
1 2 3 4 Anti-Money Laundering (AML) identifies fraudulenttransactions by looking to find similarities in operations against the Deny list established by thegovernments or regulators. In a fraction of a second, accelerated AML can check 6M-entry lists while current systems analyze 10,000 entries only. All this happens in real time, when the transactionsare taking place. When the system identifies a fraudulent transaction,it flags it for expert analysis.

Key benefits

Faster data access and calculations in real time Reduction of manual intervention for improved cost efficiency Elimination of batch processes Improved accuracy and resolution of false positives
Faster data access and calculations in real time Reduction of manual intervention for improved cost efficiency Elimination of batch processes Improved accuracy and resolution of false positives

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