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Ellisa helps funds remitters monitor customers, screen entities against sanctions and PEP lists, and detect fraud through transaction pattern analysis, enhancing AML/CTF compliance.
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Empower your compliance strategy with advanced data intelligence and risk insights.
Designed for banks, remitters, and other financial institutions, this solution pinpoints high-risk profiles and suspicious entities that could pose legal or reputational threats.
By analyzing expansive datasets, it uncovers hidden connections so you can address potential liabilities before they escalate.
Each record is accurately compiled, verified, and de-duplicated to present a clear view of possible corruption, money laundering, or other illicit activities.
The platform’s transparent approach removes guesswork, helping your team act on timely, well-founded information.
Drawing on 1,792,495 entities from 274 international sources, the system continually updates its records to keep pace with evolving regulatory requirements.
Whether it’s scanning official sanctions lists or tracking politically exposed persons (PEPs), the coverage spans a world of potential risks, ensuring your organization remains compliant across all markets.
Consolidated list of sanctioned entities designated by different countries and international organisations. This can include military, trade and travel restrictions.
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Pre-written phrases guide you and help you stand out.
Pre-written phrases guide you and help you stand out.
Pre-written phrases guide you and help you stand out.
Ellisa transforms vast datasets into actionable insights, categorizing individuals, entities, and activities to provide a holistic view of potential risks.
With over 970,000 individuals and 173,000 companies, Ellisa’s database spans a wide spectrum of profiles, from politicians and heads of state to sanctioned entities and financial institutions.
It also captures key sectors such as municipal governments, regulatory bodies, and military organizations, ensuring no critical detail is overlooked.
Ellisa doesn’t stop at people and companies.
It tracks 96,000 organizations, 28,000 addresses, 12,000 cryptocurrency wallets, and even physical assets like airplanes and vessels, creating a complete picture of interconnected risks across all domains.
Ellisa’s dataset highlights high-priority risks, including:
600,554 politicians to identify politically exposed persons (PEPs).
75,246 sanctioned entities and 65,619 sanction-linked entities to ensure compliance.
11,184 financial crime cases and 4,935 terrorism links for targeted investigations.
Through advanced categorization, Ellisa pinpoints risks like 9,433 trade risks, 1,724 state government actions, and 446 cases related to central banking and financial integrity.
Specialized data on cybercrime, trafficking, and spy networks ensures that even the most elusive threats are brought to light.
By mapping risks across entities, sectors, and activities, Ellisa empowers banks, remitters, and other financial stakeholders to identify and act on hidden threats with speed and precision.
Ellisa achieves a fraud detection accuracy rate of over 84%,
significantly reducing the risk of financial loss due to fraudulent activities.
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.
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.
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 examines transactions from multiple angles to identify high-risk patterns that may go unnoticed in conventional systems. Key factors include:
Detects deviations from typical behavior based on historical customer profiles or industry benchmarks.
Flags transactions involving high-risk countries or regions inconsistent with the customer’s profile.
Identifies attempts to divide large sums into smaller amounts to avoid detection thresholds.
Monitors closely timed transactions that suggest coordination or layering of illicit funds.
Detects shared beneficiaries, repeated counterparties, or accounts with similar transaction patterns.
Flags transactions labeled with vague reasons, such as "donation" or "consulting," often used to obscure illicit activity.
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.
