Wednesday, 11 September 2024

QUANTEXA NAMED CATEGORY LEADER IN CHARTIS' REPORT FOR ENTERPRISE FRAUD SOLUTIONS

KUALA LUMPUR, Sept 10 (Bernama) -- Quantexa announced it has been recognised as a Category Leader in the RiskTech Quadrant for Enterprise Fraud Solutions in Chartis Research’s Enterprise and Payment Fraud Solutions, 2024 Market Update and Vendor Landscape report.

In a statement, Quantexa said the positioning as a leader reflects its strong performance across multiple key criteria, particularly in advanced fraud detection techniques and its robust platform for fraud analytics.

Its Global Head of Fraud Solutions, Ivan Heard said being ranked among other category change-makers by Chartis inspires the company to continue exceeding in advanced, artificial intelligence (AI)-driven fraud detection techniques.

“The recognition that we are a best-in-class solution validates that we are providing our customers with some of the strongest capabilities in emerging fraud types like APP and mule detection,” said Heard.

The annual Chartis Market Update and Vendor Landscape report notes several key themes in the market for anti-fraud solutions which include the growing role of generative AI in automation and the co-piloting of fraud solutions.

In addition, the report explains how vendors are using new cutting-edge technology to address their clients’ evolving needs in the fraud landscape.

Quantexa’s inclusion as a Category Leader recognises its innovative and comprehensive platform approach, integrating workflow with knowledge graph analytics and addressing the constant evolution of challenges in identity risk and application fraud.

The company has also been recognised as one of the top vendors in the inaugural Chartis RiskTechAI 50 2024 ranking and research report, whereby Quantexa technology has been commended in two categories, namely ‘Advanced tax fraud capabilities’ and ‘AI for government applications’.

This recognition underlines Quantexa's industry leading expertise in leveraging AI for specialised applications in tax fraud detection and government use cases.

-- BERNAMA

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