Don't let fraud erode your bottom line, damage your reputation, or stall your growth. FraudNet's AI-driven platform empowers enterprises to stay ahead of threats, streamline compliance, and manage risk at scale—all in real-time. While fraudsters evolve tactics, our platform detects tomorrow's threats, delivering risk assessments through insights from billions of analyzed transactions.
Imagine transforming your fraud prevention with a single, robust platform: comprehensive screening for smoother onboarding and reduced risk exposure, continuous monitoring to proactively identify and block new threats, and precision fraud detection across channels and payment types with real-time, AI-powered risk scoring. Our proprietary machine learning models continuously learn and improve, identifying patterns invisible to traditional systems. Paired with our Data Hub of dozens of third-party data integrations, you'll gain unprecedented fraud and risk protection while slashing false positives and eliminating operational inefficiencies.
The impact is undeniable. Leading payment companies, financial institutions, innovative fintechs, and commerce brands trust our AI-powered solutions worldwide, and they're seeing dramatic results: 80% reduction in fraud losses and 97% fewer false positives. With our flexible no-code/low-code architecture, you can scale effortlessly as you grow.
Why settle for outdated fraud and risk management systems when you could be building resilience for future opportunities? See the Fraud.Net difference for yourself. Request your personalized demo today and discover how we can help you strengthen your business against threats while empowering growth.
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Highcharts, a Javascript-based charting library, makes it easy to add interactive charts and graphs to web or mobile projects of any size.
Highcharts is used by more than 80% of the 100 biggest companies in the world, as well as thousands of developers from a variety of industries, including finance, publishing, application development, and data science.
Highcharts is in active development since 2009. It remains a favorite among developers due to its robust feature set and ease-of-use documentation, accessibility features and vibrant community.
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Minitab Statistical Software
Our namesake product, Minitab Statistical Software, leads the way in data analysis with the power to visualize, analyze and harness your data to gain insights and solve your toughest challenges. Access trusted, proven and modern analytics combined with dynamic visualizations to empower you and your decisions.
The latest version of Minitab Statistical Software includes access to Minitab on the cloud so you can analyze from anywhere, and Graph Builder, our new interactive tool to instantly create multiple graph options at once. Minitab offers modules for Predictive Analytics and Healthcare to boost your analytics even further.
Available in 8 languages: English, Chinese, French, German, Japanese, Korean, Spanish, and Portuguese.
For 50 years, Minitab has helped thousands of companies and institutions spot trends, solve problems, and discover valuable insights in their data through our comprehensive, best-in-class suite of data analysis and process improvement tools.
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MLBox
MLBox is an advanced Python library designed for Automated Machine Learning. This library offers a variety of features, including rapid data reading, efficient distributed preprocessing, comprehensive data cleaning, robust feature selection, and effective leak detection. It excels in hyper-parameter optimization within high-dimensional spaces and includes cutting-edge predictive models for both classification and regression tasks, such as Deep Learning, Stacking, and LightGBM, along with model interpretation for predictions. The core MLBox package is divided into three sub-packages: preprocessing, optimization, and prediction. Each sub-package serves a specific purpose: the preprocessing module focuses on data reading and preparation, the optimization module tests and fine-tunes various learners, and the prediction module handles target predictions on test datasets, ensuring a streamlined workflow for machine learning practitioners. Overall, MLBox simplifies the machine learning process, making it accessible and efficient for users.
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