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AML and Financial Crime Edition | Robolitics.com

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20 Birchin Lane +44 (0) 203 006 6405
Real time monitors for standard Money Laundering and Financial Crime behaviour, providing you with the ability to halt transactions and carryout manual checks prior to authorisation.

It also carries out monitors clients against PEP and Sanction List on an ongoing basis using Machine Learning to manage repetitive alerts reducing the overhead of repeat rejection of False Positives.
Our AML edition is heavily focused on reducing “False Positives” whilst also reducing case or “alert management” time. This sophisticated workflow engine surfaces a comprehensive set of information about the transaction as well as associated and historic data. Supporting the compliance team achieve a higher OneTouch – Close or Escalate ratio.
Ai and Machine Learning
Reduce False Alert Volumes

Robolitics™ uses AI and Machine learning techniques to reduce the number of alerts. Machine learning is especially powerful in eliminating repetitive workloads on recurring false positives. AI is very effective at eradicating false alerts or categorise and cluster issues so that the number of items escalated can be reduced.

One Touch
Making Cases Actionable

Robolitics™ surfaces a more comprehensive set of data and provides the the historic perspective of issues simultaneously. This enabling Compliance Officers to complete investigations faster. Additionally, clustering and dip testing can be used to process multiple alerts simultaneously.

Leveraging Your Investment

Robolitics™ is a multi regulation monitoring platform exploiting the investment across other areas or compliance, including: KYC Transaction Monitoring GDPR

Robolitics uses Artificial Intelligence and High speed analytics to identify issues and create alerts. 
Our solution learns on the job looking for patterns to help rule in, or out Items that may initially fit the broad categorisation of a risky transaction.
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Automated Machine Learning

Robolitics™ technology automatically detects fraud patterns using AI and Deep Learning algorithms and pattern recognition.

Interactive Data Model

Supporting the business to interact with the data to understand sensitivity and drivers across single or multiple alert categories. 

Risk Based Categorisation

Managing sensitivity by client risk category. Dialling up sensitivity for low risk clients and dialling up for low risk clients.  These sensitivities can also be modified depending upon real time transactional behaviour.

Creating Alert Clusters

Simplify and reduce effort of analysis review and reporting / closing. (Dip testing and bulk review).

Outcome based learning

Using Robolitics™ text base analytics of alert workflow to create dynamic categories and build clusters based around these categories. 

Deep Learning

Identifying broader patterns looking across all data. Robolitics™ is able to identify patterns in the data.