AI, ML-powered Fraud Detection solution to identify payment anomalies and block fraudulent transactions with never before seen accuracyContact us
Global annual losses from fraud generated by card-based payment systems
It is projected that fraud losses will rise a further 25% and exceed $40 billion by 2027
Cases of credit card fraud being reported in the UK alone in 2020
Rule-based systems and fraud detection machine learning algorithms are two completely different approaches to combating illicit payments. Rule-based systems use pre-programmed behavior scripts to identify suspicious transactions. Rule-based systems become more and more expensive to maintain as they grow larger and more complex.
Machine learning fraud detection systems offer a much more proactive and flexible approach to identifying fraudulent payments. By replacing outdated rule-based systems with modern machine learning solutions, banks and payment processors can reduce the losses they incur due to fraud, lower their security system-related expenses, and reduce payment friction for their clients.
SDK.finance Anomaly Detection solution 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.
As the system is exposed to more data and new instances of fraud, it only becomes better at separating fraudulent payments from genuine transactions. This means that it can potentially require fewer manual reviews per million transactions as time goes on.
Using heaps of rules layered upon rules results in more and more genuine transactions being blocked by the traditional system. ML algorithms do not suffer from this problem.
Payment fraud detection machine learning systems are much more flexible in nature. They can understand data sets in a way no human ever could.
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.
No data sharing. All your sensitive data is stored on your own secure servers. Full control in your hands.
Graphical summaries, powerful dashboards, and real-time alerts are built into the system for better transaction monitoring and fraud prevention.