Product Overview

With the increasing cost of compliance and heightened pressure from regulators globally, financial institutions are being forced to be more innovative in handling compliance in a demanding and dynamic market. Moreover, watchlist screening is becoming ever more complex, with false positive rates increasing for many institutions. The amount of false positive alerts adds to the increased cost and greater risk of human error.

Leveraging the AI disciplines of Natural Language Processing (NLP) and Machine Learning (ML), our Self Learning Optimisation solution easily integrates with your existing watchlist screening solutions to drastically reduce false positives, cutting compliance costs and delivering reputational protection across all payment processes.

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75% false positive reduction

False positives are reduced by 75% on average, within 6 months.


Reduce operating costs and risk

With AI-based technology that mimics human decision-making processes, freeing your team to focus on more productive tasks.

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Explainable & auditable

Full transparency enables you to understand the reasoning behind decisions, essential for auditing.

How it works

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Reduce false positives with your existing screening tool

Using AI disciplines, our solution analyses and understands users’ actions to learn the human reasoning from solved alerts, thereby reducing false positives and presenting you with an explanation for approval - along with continuous and supervised learning.

This decrease in the number of false positives reduces the cost to compliance as well as the risk of human error.

Self Learning Optimisation uses advanced AI-based technologies that work with your existing third-party screening tools.

Product highlights:

  • Captures, analyses and learns from user actions and payments attributes.
  • Ongoing supervised learning on how alerts are resolved.
  • Full control by authorised users in configuring the engine logic, parameters or threshold authorisations.
  • Explainable and auditable actions.
  • Fine-tune alert generation using our extensive knowledge base library of rule-like understanding models.
  • Decision-making models are built on over 20 years of payments and sanctions screening experience.

Solution Capabilities

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Screening Solution Optimisation

Lower false positive rates through machine learning, reduced false alerts through NLP and automated 4-eyes control.

Natural Language Processing

Gain analysis of the context of historical payments and real-time flows to eliminate false alerts generation.

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Machine Learning

False positive reduction models are created based on historical payments, and reinforced learning on live payment flows.

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Explanation validation

Review and approval of explanations prior to activation to ensure alignment with your risk needs.

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Four eyes control

Self learning optimization acts as the 2nd pair of eyes for appropriate risk mitigation and to lower costs.

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Explainable & auditable

Understanding the reasoning behind decisions, needed for auditing.

Key Benefits

Protects your investment

Our solution works alongside your existing third-party screening engine; improving the results and protecting your investment.

AI-powered solution

Mimics the human decision-making process to ensure appropriate decision making.

False positive reduction

False positives are reduced by 75% on average, within 6 months.

Provides full Evidence

Our solution provides full transparency and evidence and is auditable by internal auditors and regulators.

Real-time Capability

Self Learning Optimisation enables you to reduce false positives of real-time payments schemes.

In line with your risk needs

Our model authorisation module enables you to review and approve models prior to activation, to ensure alignment with your risk appetite.

Find Out More

Built for simplicity and scalability, you can integrate Pelican in as little as four weeks. Reach out and our global team will help you find the right solution for your business.

Brochures and Datasheets

For more information download our brochure now.


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