--- title: AI Agents - Fraud source_page: https://www.sardine.ai/agentic-ai-for-fraud canonical: https://www.sardine.ai/agentic-ai-for-fraud format: text/markdown description: --- **Quick links:** [Human page](https://www.sardine.ai/agentic-ai-for-fraud) · [Home](https://www.sardine.ai) · [Customers](https://www.sardine.ai/customers) · [Blog](https://www.sardine.ai/insights/blog) · [Demo](https://www.sardine.ai/demo) --- # AI Agents - Fraud ## AI agents built for fraud operations Complete deep analysis in minutes, fine-tune fraud monitoring rules, respond to new attacks, and automate dispute filing. ## Agents integrated with your data and fraud workflows ### Investigate and stop a fraud attack in minutes The agent analyzes the device, behavioral, and traffic signals behind a fraud attack to identify accounts linked to the same pattern or fraud ring. It then recommends the rules needed to stop it. - Anomaly Detection Agent - Data Analyst Agent - Graph Analyst Agent - OSINT Search Agent Data Analysis ### Easily spot fraud rings and coordinated attacks Maps connections across users, devices, addresses, payment instruments, and other shared identifiers to uncover fraud rings, multi-accounting, promo abuse, bust out fraud, and other coordinated patterns. The agent helps make sense of complex linked behavior in minutes, so you can act on coordinated fraud faster and spend less time manually piecing together networks. - Graph Analyst Agent - Data Analyst Agent Fraud Ring Detection ### Quickly write and optimize fraud monitoring rules Create, test, and optimize rules without engineering or vendor support. Describe a rule in any language, and AI automatically turns it into logic. The agent also recommends threshold adjustments and identifies noisy or underperforming rules based on traffic patterns to improve fire rate and alert quality. - Rule Assistant Agent - Data Analyst Agent Rule Optimization ### Automate dispute handling and evidence collection Gathers transaction details, customer history, device data, and delivery proof for each dispute. The agent then builds clear, network-ready packages for each reason code, with consistent explanations tailored to the dispute type. Over time, the agent learns from win/loss results to improve future responses. - Dispute Filing Agent Dispute Management ## Unlock the full potential of Sardine’s risk platform Our agents know how to apply the platform’s most advanced capabilities so teams can operate with expert precision and less manual work from day one. ### Device & Behavior Analyze every customer touchpoint to identify advanced fraud patterns, without adding friction. ### Machine Learning Trained on billions of data points to protect onboarding, login, and payment flows. ### Consortium Data Cross-industry intelligence from our consortium spanning billions of devices and consumers. ### Connections Graph Map relationships between users, devices, IP addresses, phone, email, and transactions. ### Rules & Workflows Easily create, test, and deploy rules without engineering or vendor support. ### Case Management Centralize alerts, transactions, entities, and decisions in one integrated view. ## Featured resources

FRAUD PREVENTION

Agentic AI for Fraud

AI agents built for fraud operations

Complete deep analysis in minutes, fine-tune fraud monitoring rules, respond to new attacks, and automate dispute filing.

AI agents that continuously strengthen your defenses

Spot complex fraud patterns earlier, reduce investigation time, and give teams clearer signals for faster, more accurate decisions.

Identify merchants with a high concentration of shared users, and determine whether there are links between those merchants that could indicate collusion.

In the last 60 days, identify merchants that have been dormant but are now experiencing a spike in session activity.

Show the top 10 device IDs shared by users, map the geographic locations associated with those users, and identify any session-based risk signals.

Perform an OSINT search on and return any relevant findings. 

Review activity from the last 30 days and identify potential account takeover cases.

Identify merchants with a high concentration of shared users, and determine whether there are links between those merchants that could indicate collusion.

In the last 60 days, identify merchants that have been dormant but are now experiencing a spike in session activity.

Show the top 10 device IDs shared by users, map the geographic locations associated with those users, and identify any session-based risk signals.

Perform an OSINT search on and return any relevant findings. 

Review activity from the last 30 days and identify potential account takeover cases.

Perform an OSINT search on and return any relevant findings. 

Determine whether any of my disputes qualify for Visa Compelling Evidence 3.0. If the dispute qualifies, provide the relevant details for all three transactions.

In the last 30 days, identify users who made low-value transactions at one merchant and then high-value transactions at another merchant.

Suggest a rule for merchants that are under 90 days old, have no acquiring transactions, and are using a device that has been used by more than five accounts.

Determine whether any of my disputes qualify for Visa Compelling Evidence 3.0. If the dispute qualifies, provide the relevant details for all three transactions.

In the last 30 days, identify users who made low-value transactions at one merchant and then high-value transactions at another merchant.

Identify merchants that have been dormant in the last 60 days but are now showing a spike in session activity.

Review activity from the last 30 days and identify potential account takeover cases.

Review the past several months of activity for the email addresses below. Identify the merchants associated with those emails, and determine whether there are common merchants or signs of collusion that may indicate a fraud ring.

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