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Ellisa helps funds remitters enhance AUSTRAC compliance, detect fraud more effectively, and improve AML/CTF efficiency
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Ellisa achieves a fraud detection accuracy rate of over 84%,
significantly reducing the risk of financial loss due to fraudulent activities.
Ellisa offers industry-leading performance in sanctions and watchlist screening, utilizing over 190 sanctions and PEP lists.
This helps institutions achieve compliance with unparalleled confidence, speed, and accuracy.
Instant alerts for suspicious activities with quick, accurate analysis.
Identifies high-risk transactions using external data and ensemble methods to reduce false positives.
Handles large datasets and adapts to varying data types, ensuring compliance with dynamic updates.
This framework draws heavily on how the human brain processes information
The brain stores and recalls patterns, forming a base of what is normal or expected.
The brain connects related information, understanding context and relationships, which helps in making sense of complex situations.
The brain identifies deviations from established references, focusing on whether new information aligns with what is expected or signals a potential issue.
Ellisa utilizes a range of advanced machine learning models to detect and prevent fraudulent activities. These include:
Capable of learning complex patterns and adapting to non-linear data relationships.
Our anomaly detection techniques, including Isolation Forests and Autoencoders, are crucial in identifying outliers and unusual patterns that traditional systems might miss.
Known for robustness and handling numerous features and interactions.
Offers high accuracy and optimizes various loss functions.
