AI & Machine Learning

Future-Proof Your Business with AI

Empower your team to make faster, more accurate fraud and risk decisions using cutting-edge AI and Machine Learning.

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What We Do

Turning Complexity Into Clarity with AI-Powered Solutions

Your unique fraud and risk challenges deserve solutions that fit. FraudNet’s AI and Machine Learning capabilities are core to our platform, built to adapt, detect, and defend against fraud and financial crime.

Supervised Machine Learning

Custom machine learning models help spot hidden patterns in your data, making it easier to detect and prevent fraud. By analyzing cases and scoring risks accurately, they keep you one step ahead.

Anomaly Detection

Spot new risks sooner with Anomaly Detection. Identify unusual behaviors and patterns that deviate from the norm, flagging risks you haven’t encountered before.

Graph Neural Networks

Discover hidden connections between entities to quickly detect high-risk relationships, delivering deeper insights for smarter decision-making and to reduce potential risks to your business.

AI Agents

Free your team to focus on what matters most. Automate routine tasks, extract meaningful data at scale, and drive faster, smarter outcomes in fraud analysis with the help of AI Agents.

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Benefits

Strengthen Fraud and Risk Management

Experience the power of AI and Machine Learning to deliver real-time decisioning, full-time protection, and high-impact results.

Reduce False Positives

Increase Approvals

Detect More Fraud

Automate Routine Tasks

How We Do It

Transforming Insights Into Decisive Action

AI doesn’t just enhance your fraud and risk program—it adds a new dimension. Move faster, reduce uncertainty, and make every decision count. 

Fraud Detection

Detect and prevent threats before they occur. Leverage models tailored to your specific risk priorities to identify fraudulent activity with precision.

Anomaly Detection

Spot unusual patterns in transactions and behaviors, revealing risks outside traditional scenarios and helping your team uncover new risks before they take hold.

Real-Time Risk Scoring

Risk score for every transaction in real time with customized machine learning models that deliver highly-accurate, context-driven scores.

Intelligent Automation

Accelerate your decision-making by automating routine approvals or rejections, and streamline investigations to boost efficiency across your team.

Optimized Rules

Combine machine learning with rules-based systems for accurate risk scoring and decision-making that evolves alongside your needs.

Bar chart showing a total fraud count of 27,912 divided into categories: Very Low, Low, Medium, and High risk levels with corresponding color segments.
Why FraudNet

The Edge You Need to Stay Ahead

What sets our AI and Machine Learning approach apart? It’s more than technology—it’s foundational to our solutions and is paired with expertise and customization tailored to your business goals. 

Explainable & Transparent

Gain full visibility into every transaction and event, and understand each AI model generated risk score with detailed data and built-in explainability.

Customization Done Right

Your challenges are one-of-a-kind. Off-the-shelf tools don’t cut it. By tailoring machine learning models to your specific risk priorities, you’ll gain sharper, more accurate results. 

Data-Driven Outcomes

Better data equals stronger decisions. From your internal data to diverse external sources, our powerful data ecosystem drives unmatched accuracy and intelligence. 

Patented Machine Learning Process

Our patented approach delivers proven results grounded in advanced data science. Trust in technology backed by excellence. 

Case Study

AI in Action: BNPL Pioneer Tinka Reduces Account Takeovers by 90%

FraudNet delivers rapid authorization scoring in under 100 milliseconds, leveraging global signals to intercept emerging threats before approval, ensuring revenue protection without added friction.

  • Scores every authorization in less than 100 ms
  • Learns from global signals to identify emerging threats
  • Protects revenue while maintaining a frictionless experience

Get Started Today

Experience how FraudNet can help you reduce fraud, stay compliant, and protect your business and bottom line.

Resources

Recognized by Industry Analysts

FAQs

How is AI used for fraud detection?

AI is used for fraud detection through several complementary techniques: supervised machine learning models spot patterns in historical data, anomaly detection flags behavior outside the norm, and graph neural networks map hidden connections between entities to catch coordinated fraud rings. FraudNet layers these approaches together rather than relying on a single model type, since different fraud patterns respond to different techniques.

What is AI for fraud detection, and how does it work?

AI for fraud detection learns from transaction, identity, and behavioral data, then scores new activity against those learned patterns in real time, catching evolving tactics that a fixed rule set would miss. AI for fraud detection is only as strong as the data behind it, which is why FraudNet builds explainability directly into every risk score rather than treating models as a black box.

How does fraud detection machine learning differ from rules-based systems?

Fraud detection machine learning models adapt from data over time, while rules-based systems execute exactly what's manually programmed and stay static until someone updates them. FraudNet combines both, using rules for clear policy enforcement and machine learning for the fraud patterns that a fixed rule set alone would miss, then feeds the result into intelligent risk decisioning.

What is AI fraud prevention, and how effective is it?

AI fraud prevention uses machine learning to stop fraud before it completes rather than only flagging it afterward, scoring transactions in real time and triggering blocks or step-up checks for high-risk activity. FraudNet's AI fraud prevention has delivered documented results including a 90% reduction in fraud rate for a multi-national payments company and a 90% reduction in account takeovers for a leading BNPL provider.

Can AI fraud detection reduce false positives without missing more fraud?

AI fraud detection can meaningfully reduce false positives when the underlying model is well-calibrated, since anomaly detection flags behavior that's genuinely unusual for a specific entity rather than applying one fixed threshold to everyone. FraudNet customers have documented results including a 98% reduction in false positives for a global payment processor and a 90% reduction for a European fintech.