Unsupervised Machine Learning
Detects emerging fraud attacks
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AI Fraud Detection and Prevention Outcomes
Prevent Emerging Fraud
Next-generation AI protects against new and existing fraud attacks before they become chargebacks.
Accept More Good Orders
Improve the customer experience, increase order acceptance rates, and generate more revenue while reducing false positives.
Reduce Manual Reviews
Lower operational costs and drive greater scalability to fraud operations teams by automating approve and decline decisions.
Control Business Outcomes
Customize risk thresholds to achieve desired business outcomes: decline rates, fraud rates, and fraud operations costs.
AI That Simulates an Experienced Fraud Analyst
Kount’s AI simulates an experienced fraud analyst by weighing the risk of fraud against the value of the customer, but on a faster and more scalable basis.
Supervised Machine Learning
Learns from past decisions
Identity Trust Global NetworkTM
Fuels machine learning models
Command Center
Fine-tunes fraud prevention decisions
OmniscoreTM: AI-Driven Transaction Safety Rating
The score that catches more fraud
Omniscore is the actionable transaction safety rating that is produced by Kount’s AI. It’s twice as effective than previous models at detecting payments fraud, while maintaining Kount’s 250 millisecond response rate.
The highly predictive score can be relied upon when decisioning orders, so that there is less reliance on manual review and reactive fraud rules. The result is less false positives and negatives, and more good transactions to generate revenue.
Download Kount's AI-Driven Fraud Prevention Datasheet
Unsupervised Machine Learning
Detects emerging fraud attacks
Kount’s unsupervised machine learning uses advanced algorithms and models to detect transaction anomalies much faster, more accurately, and on a more scalable basis than human judgment alone. It catches emerging fraud attacks that supervised machine learning models can’t catch before they result in chargebacks.
Supervised Machine Learning
Learns from past decisions
Kount’s supervised machine learning analyzes billions of historical transactions from the Identity Trust Global Network TM and looks for signals that have predicted fraud in the past.
Identity Trust Global Network TM
Fuels machine learning models
Kount’s Identity Trust Global Network links signal data from 32 billion annual interactions across 250 countries and territories, 75+ industries, 50+ payment processors and card networks, and decisions from thousands of fraud analysts. That analytical richness feeds into machine learning models so they are properly trained to accurately predict risk.
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Command Center
Fine-tunes fraud prevention decisions
The Command Center case management system provides the ability to fine-tune fraud prevention decisions, conduct investigations, and monitor performance. It enables customers to create rules and policies that meet their unique business needs and customize risk thresholds to address emerging attack methods, new use cases, and issues such as card testing.
Self-Service Analytics
Actionable business intelligence reporting
Datamart is the analytics and reporting functionality within the platform that enables reporting on the rich data points collected from payment transactions, customer interactions, and outcomes. It allows in-depth investigation into suspicious behavior as well as business performance. New discoveries can inform future rules and policies created within the Kount solution, and can also provide a breadth and depth of customer knowledge.
