Detecting and Preventing Fraud with AI

Fraud is evolving, and so is the technology to fight it. At TechTarget, we showcase how Artificial Intelligence is transforming fraud detection from reactive response to real-time prevention across industries like banking, e-commerce, insurance, and more.

$42 Billion

Lost globally to fraud each year, and growing

90% Financial Firms

Use AI to detect fraud in real-time

4x Faster Detection

AI spots fraudulent activity significantly quicker than traditional methods

How AI Detects Fraud?

AI-powered fraud detection is more than just alerts. It’s predictive, proactive, and always learning, helping businesses stay ahead of increasingly sophisticated threats.

Pattern Recognition: AI identifies unusual behavior and outliers in transaction patterns.
Real-Time Monitoring: Detects and flags suspicious activity as it happens, not after the damage is done.
Adaptive Learning: AI systems get smarter with every new data point, learning from past attacks.
Automated Risk Scoring: Assigns fraud probability to transactions so companies can prioritize investigations.

Safeguarding Resources Through AI Fraud Detection

From banking to retail, AI is redefining security protocols to counter fraud attempts beforehand.

Banking & FinTech

AI systems like Feedzai and Darktrace flag anomalies across billions of transactions in real-time.

E-commerce

AI tools such as Forter verify users and block fake accounts or transactions instantly.

Insurance

Detects exaggerated or fake claims using image recognition and behavioral analysis.

Healthcare

Monitors for duplicate billing, false patient records, and identity theft.

Telecom & Utilities

Prevents SIM swaps, subscription fraud, and device cloning using AI analytics.

Leading AI Tools for Fraud Detection

Tools and frameworks that power secure ecosystems using artificial intelligence and machine learning technologies.

SAS Fraud Management Enterprise-grade fraud detection for banks and insurers.
Kount (by Equifax) Combines AI and device fingerprinting to stop e-commerce fraud.
DataVisor Applies unsupervised ML to detect unknown and evolving fraud patterns.
Zelros AI for insurance fraud that integrates with claims systems for faster verdicts.
Amazon Fraud Detector Pre-trained ML models for real-time fraud scoring, no ML expertise required.

We Explain Fraud Detection AI in Detail

At TechTarget, we break down the key concepts that drive intelligent fraud prevention

Anomaly Detection

Finding what doesn’t fit — unusual spikes, behavior, or data.

Supervised ML

Training models on known fraud cases to identify similar future threats.

Unsupervised ML

Detecting new and unknown fraud patterns without labeled data.

Neural Networks

AI that mimics the human brain to recognize complex fraud signals.

Decision Trees

Simple, interpretable models are used in fraud scoring systems.

Behavioral Biometrics

AI that tracks how users type, swipe, or interact, making it hard for fraudsters to impersonate.

False Positives / False Negatives

Balancing accuracy to avoid blocking genuine users or letting fraud slip through.

Why AI-Based Fraud Detection Is the Future

Traditional fraud systems are rules-based and easy to bypass. AI adapts, scales, and improves every second. At TechTarget, we help businesses harness this power to stay one step ahead.