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Boston MA Office

265 Franklin Street, Floor 5
Boston
MA
US

Guildford Office Office

Waterside House Surrey Research Park Surrey
Guildford
Surrey
GB

London Office Office

Blue Fin Building 110 Southwark Street London
London
United Kingdom
GB

Dublin Office Office

Dundrum Town Centre Block 4 Sandyford Road Ireland
Dublin
Dundrum
IE

Canada Office Office

2nd Floor, 154 University Avenue Toronto Ontario M5H 3Y9 Canada
Toronto
Ontario
CA

Melbourne Office Office

Level 26, North Tower 459 Collins Street Melbourne VIC 3000 Australia
Melbourne
AU

Sydney Office Office

Level 12 20 Bridge Street Sydney NSW 2000 Australia
Sydney
AU

Kuala Lumpur Office Office

Level 28, Menara Binjai 2 Jalan Binjai Kuala Lumpur 50450 Malaysia
Kuala Lumpur
MY

Singapore Office Office

Unit No. #42-01 Office Tower 1 One Raffles Place 048616 Singapore
Singapore
SG

Dubai Office Office

Office 905 Arjaan Office Tower PO Box 500523 Dubai Media City United Arab Emirates
Dubai
AE

Gloucester Office Office

1270 Lansdowne Court Gloucester Business Park Gloucester GL3 4AB
Gloucester
GB

Leeds Office Office

Avalon House, Savannah Way Leeds Valley Park Leeds LS10 1AB United Kingdom
Leeds
GB

Poland Office Office

Malta Office Park ul. Abpa A. Baraniaka 88 Poznan 61-131 Poland
Poznan
PL

McLean Office Office

1676 International Drive, Suite 1000, McLean, VA 22102
McLean
VA
US

Telephone

02078124239

Contact

Sital Shah
[email protected]

NetReveal AML Optimisation

The NetReveal AML Optimisation solution is a self-service module that gives financial institutions the ability to define, monitor, and deploy detection profiles for improved anti-money laundering model accuracy, effectiveness and lower false positive alerts without reducing true positives.

Financial institutions are overwhelmed with the increasing volume of alerts generated by mandatory transaction monitoring systems and are unable to maximise their investigative resources. Industry estimates suggest at least 95% of the alerts are false positives. Identifying and reporting on truly suspicious activity is getting harder and putting greater burden on investigation teams.

Financial institutions using outdated detection models that remain unchanged from their original creation and implementation may be leaving many suspicious transactions undetected. Constantly evolving threats require continuous scenario improvement.

Regulatory authorities are increasingly leaning on institutions to not only justify the rationale behind AML model changes, but also to validate the efficacy of existing models and the assumptions on which those models were built as part of a wider model risk governance program.

Key Features include: 

  • Profile store manager for AML Optimisation big data – compliance analysts can define, monitor, and deploy profiles directly within the solution to improve and speed up decisions. Easily simulate true positive and false positive rate changes within the production scenarios
  • Machine learning algorithms – can be scheduled to review detection results of rule-based monitoring systems and propose the optimal parameters that make up those rules. The approach significantly reduces false positives, improves efficiency, and lowers operation costs
  • Model management and audit logging – optimises model governance by giving analysts the ability to demonstrate to auditors and management their rationale and justification behind decisions made for scenario model changes
  • User-friendly interface – a packaged, easy to use solution, that’s designed specifically for analysts, not data scientists or engineers. Parameters can be tuned, analysed and tested in a visual way for quick interpretation and remediation

Contact us for more info!

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