Find and fix key high-risk incidents as they are happening. AML + KYC + Transactions Anomaly Detection = the best combination of fraud detection toolsContact us
Global annual losses from fraud generated by card-based payment systems
Global penalties for AML failures in 2019 with an average of $143.5 million per fine
Of all fines are for anti-money laundering and KYC violations
Financial institutions and banks have to prevent financial losses and fraud at the earliest stage using fraud detection tools.
Sanctions monitoring, Know Your Customer (KYC), Anti Money Laundering (AML) are good tools for fraud prevention, but they are not enough. As cybercrime becomes more sophisticated, financial companies need to incorporate strong fraud-prevention mechanisms into their strategies to stay ahead.
Transactions Anomaly Detection powered by Machine Learning technology monitors customer transactions and classifies data into normal distribution and outliers. Combined with an analysis of customers’ historical information and profiles, it is possible to accurately determine potentially fraudulent transactions.
This combination of tools is the best option to prevent fraud and minimize fraud-related risks.
Verify that a customer’s name, birthday, address, and Social Security number all match. KYC checks are a good starting point to verify that those four pieces of information are associated with one another
Verify each new customer against a list of politically exposed persons (PEPs), sanction lists, and criminal databases that are issued by global law enforcement agencies
Monitors customer transactions on a daily basis or in real time for risk. ML algorithms are used to find very subtle and usually hidden events and correlations in user behavior that may signal fraud
Enables companies to immediately respond to deviations from the norm, potentially saving millions that would have been lost to fraud otherwise. Eliminates the delay between spotting the problem and resolving it.
Artificial Intelligence, Machine Learning, and Deep Learning technologies allow to classify data into normal distribution and outliers. When a transaction deviates from a dataset’s normal behavior, it can be considered potentially fraudulent.
Evolves automatically based on the insights acquired from newer incidents. As the database grows larger, the number of patterns identified also increases, leading to more accurate insights.
Seamlessly integrates with existing systems, be it a data warehouse, or a sophisticated BI system via APIs.
Transaction Anomaly Detection software in the cloud. The secure, continuously evolving solution delivers faster compliance and time to market.
Graphical summaries, powerful dashboards, and real-time alerts are built into the system for better transaction monitoring and fraud prevention.