Transaction Fraud

Stay ahead of rapidly-evolving attack patterns to prevent financial loss by searching out new patterns, trends, and suspicious correlations.

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Full-Scale Protection for Fast-Moving Commerce


Transaction fraud has evolved from single-party credit card theft to a full-blown enterprise. Fraudsters now orchestrate highly-intelligent attacks with arsenals of stolen credit cards, personal information, and two-step authentication scripts. DataVisor’s approach, powered by proprietary unsupervised machine learning algorithms, does not require data or labels to detect new attack techniques. Our fully autonomous technology drastically reduces response time to fraudulent attacks.

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Card Not Present

EMV technology has forced attackers to adapt and pivot to ‘card not present’ transactions. Fight these fraudulent transactions by combining and sending structured and unstructured digital signals in real time.

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Real-Time Payments

When transferring money, speed and convenience are paramount for both customers and fraudsters alike. Early detection enables you to prevent fraudulent account openings and allows for continuous monitoring for suspicious transfer requests.

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Digital Wallets

Digital coupons, promotions, and loyalty programs drive customer engagement and retention. But one breach can undermine customer trust and result in account takeovers and financial loss. Detect and eliminate these attacks before they cause harm.

Machine Learning Reimagined


Massive numbers of ever-evolving attacks are putting significant pressure on fraud teams to try and not just keep up, but get ahead on detecting fraud. A key challenge is that existing fraud detection solutions that might work well on training and testing data are unable to keep up with sophisticated and varied new attack techniques.

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Uncover New Fraud Patterns

Surface new patterns, spot trends, and identify suspicious correlations without historic labels.

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Minimize Maintenance

Enjoy consistent performance over time without the need for retuning, and prevent decay in production.

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Enjoy Immediate Results

Integrate powerful unsupervised machine learning solutions immediately and achieve optimal performance right away.

Client Success Stories

Transaction Fraud

Learn how financial institutions are successfully saving millions in the fight against fraud.

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DataVisor Fraud Index Report: Q1 2019

Access proprietary data and research results to discover the latest attack techniques and prevention strategies.

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Why Today’s Machine Learning Fraud Detection In Financial Institutions Falls Short

We’ve developed a guide that talks about how to address the weaknesses in today’s machine learning systems using an unsupervised machine learning approach.

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Resources

Intelligent solutions. Informed decisions. Unrivaled results.

DataVisor Releases the 2019 Q1 Fraud Index Report

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DataVisor’s Q1 2019 Fraud Index Report delivers deep insights into the latest attack trends and the most effective…

DataVisor’s Q1 2019 Fraud Index Report delivers deep insights into the latest attack trends and the most effective fraud prevention strategies.

Fighting Fraud with Machine Learning: Stories from the Frontline

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5 stories. 5 victories against fraud. See how organizations across industries are proactively defeating attacks.

Organizations across industries are preserving online trust and defeating fraud with advanced machine learning solutions from DataVisor. This collection of case studies shows how clients in e-commerce, social, and financial services are defending their platforms against reputational and financial…

Keeping Up with Fraud in Digital Banking

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Discover advanced strategies for managing the rapidly-evolving fraud attacks plaguing the modern banking sector.

Innovation is a hallmark of modern finance, and as banking expands online, organizations face intense pressure to defend against massive and rapidly-evolving attacks, while simultaneously preserving frictionless customers experiences, and meeting regulatory requirements. Discover the AI-powered…


Adapt rapidly to transaction fraud.

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