Predator

Transaction Fraud & AML Transaction Monitoring

Mitigate transactional fraud and compliance risk with GBG Predator. This smart tool allows you to operate securely and launch new products and services for digital payments safely and swiftly.

 

Trusted by over 20,000 companies

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Detect and act on potential fraudulent threats with GBG Predator.

GBG Predator provides advanced fraud protection and detects potential money laundering, while focusing on supporting a frictionless journey for the majority of low-risk customers by accurately identifying anomalous behaviour during transactions and generating red flags to be investigated. GBG Predator also ensures you achieve compliance, avoiding additional operating costs and minimising financial loss for you and your business. GBG Predator works on your side so that most importantly of all, you can continue to build trust with your customers.

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Real-time fraud detection

Detect and take action to prevent fraudulent behavioural patterns by identifying threats before your customers are compromised. By leveraging advanced analytics and machine learning, you can create an intelligent workflow which has the ability to make quick real-time decisions for a seamless customer experience.

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Fraud protection readiness

Launch new payment platforms and services with fraud protection already in place, including ATM, mobile and contactless payments. Additionally, Predator allows you to leverage data from different channels to build distinctive user behaviour profiles to gain insight and detect transaction fraud.

Faster investigation processes

This transaction monitoring system provides a holistic view, meaning you can benefit from a faster and improved investigation process with inbuilt shortcuts. Block fraud, highlight transactions as genuine or suspicious, and create or edit a case within the management system effortlessly.

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Compliance without extra costs

Stay complaint with Anti-Money Laundering (AML) and counter terrorism financing regulatory requirements to avoid financial and brand damage. Our AML checks allow you to adapt and customise profiles and workflows to accommodate unique regulatory requirements by country, all the while exceeding your customers’ expectations and needs.

Additional intelligence layer

Add an additional intelligence layer with Predator's Machine Learning capability, increasing detection accuracy and allowing you to be confident in your decisioning.

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Easily add payment channels

In addition to preventing fraud losses, you have the ability to easily introduce new payment channels, such as contactless and mobile payments, with fraud protection already in place.


Frequently asked questions

Are you an existing customer? Talk to our customer support team.

GBG Predator can be used to evaluate the associated risk based on the profile of the transaction (value, type, destination, etc.), previous transactions (source, type, value) and the end customer (beneficiary). Where rules are triggered, ‘alerts’ are sent to a referral queue for investigation by a reviewer. Depending upon the findings from the investigation, the reviewer will allow the transaction to proceed or raise a case and document findings utilising the automated document reporting functions. Where required, the solution can auto-action transactions, meaning that high-risk transactions can be automatically declined and/or accounts blocked, as well as the ability to call out to other systems

GBG’s Professional Services team will use a combination of pre-defined rules and bespoke rules designed specifically to meet customer requirements. We will also pass on this knowledge so that rules can easily be maintained. As part of an implementation, we can provide an industry-proven set of standard rules that are build using our experience of working with our global customer base.

Customers can also add/configure their own rules at any time, there is no limit to the number that you can choose to apply. Users are also able to evaluate the performance of rules by running reports. GBG also provides full training with regard to how rules are managed, i.e. updated/modified, created, removed/deactivated, and tested to evaluate performance before they are deployed to a live configuration.

One of GBG Predator’s strengths is that the rules can be tested directly in the production environment, allowing you to experiment with new rules to detect new types of suspicious
Activity. Meaning transactional monitoring detection strategies can be continually improved.

Furthermore, users can implement new rule strategies immediately when required, without any unnecessary delay. Rules can be tested against a configurable number of previous transactions and run based on any predefined criteria. For example, transactions of type ‘payment’ with a value > ‘€10,000’.This enables the user to view both the number of triggered transactions and the total time duration the rule took to process.

Test runs can also be created for comparison, either between rules, based upon different criteria or following configuration changes. Testing can also be managed via implementation into a UAT environment first and then can simply be exported and imported to the Production system. The system also supports champion challenger.

A large part of GBG Predator’s ability to reduce the volume of false positives is derived from
the ability to configure the system for the various fraud and risk scenarios at various transaction touch
points.

  • Workflows: - the ability to have bespoke flows based upon transaction type/channel (or any other criteria) provides a very granular targeted assessment. This inevitably means transactions can be routed through the necessary/correct risk assessment and due diligence flow, providing a filtering approach.
  • Rules: - bespoke rules can be created and placed into grouped rulesets. This can align to different workflows and provide segmented groups of rules. Rules can also be combined with calculations and based upon a history of individual behaviour. Flagging based on the individual’s behaviour, not just generically.
  • Feedback Loop: - machine learning can be used in supervised format to provide a
    mechanism to learn from confirmed cases. Unsupervised learning can also be used to provide anomaly detection based upon large amounts of data and underlying patterns.
  • Reports: - various standard reports are available that provide information on the health of the system configuration. The ‘Efficiency of Rules’ report is designed to provide metrics on how effective each rule has been by measuring the number of rules and transactions triggered by different conditions.

 

This can be achieved through a combination of rules and visual link analysis. Rules created for this specific purpose with configurable parameters can identify and flag various scenarios. For example, the number of transactions between accounts over a specified time, the velocity, the cumulative monetary values etc. Visual Link Analysis can then graphically explain these relationships because it employs data analysis techniques to holistically examine connections/relationships and information to discover patterns and detect suspicious networks. Bespoke links can be viewed by creating SQL queries for the graph and table query, which will be displayed after clicking the analyse button in Link Analysis. Filter amounts can also be placed using monetary values to reduce or set the search criteria.

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