Payment Fraud

Future-proof
protection against
evolving payment
fraud attack
patterns

For too long, businesses have accepted fraud losses as a ‘cost of doing business’ which perpetuates the problem long term and provides the financial incentive for fraudsters worldwide.

Arkose Labs provides a more robust approach to stamping out payment fraud with a platform that combines risk-based decisioning and targeted friction. Real-time risk assessments inform a unique challenge-response mechanism, which uses incrementally complex puzzles to sap fraudsters’ resources and eliminate unauthorized payments.

Payment Fraud

Arkose Labs protects against attacks on CNP fraud, gift card abuse, unauthorized money transfers and abuse of payment details stored in consumer accounts.

PREVENT IT

Key Features

Technology Highlights

Arkose Labs provides complete protection against payment fraud. Using a long-term remediation approach, Arkose Labs breaks the economics of the attacks by dramatically increasing the time and resources needed to carry out successful payment fraud attacks.

100% Attack
Remediation SLA

Arkose Labs is the only company to offer a guarantee on stopping all automated payment attacks.

Graduated
Risk-Based Friction

Authentication challenges which evolve with attack patterns and ensure legitimate users have the opportunity to prove their authenticity.

Self-Optimizing
Platform

Continuous feedback loop between risk decisioning and enforcement challenge data fortifies the platform against evolving threats.

Shifts The
Attack Surface

The Arkose Labs platform acts as an intermediary between fraudsters and the business’ digital properties for better protection.

Read the case study

Learn how Arkose Labs provides the definitive solution
for long-term fraud prevention.

Other Use Cases

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Identify and stop automated scraping attempts with unprecedented certainty.
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Defend against mass-scale spam and phishing attempts on your websites and apps.
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See how the Arkose Labs Fraud and Abuse
Defense Platform could help your business