About the AI Fraud Analyst

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What is the AI Fraud Analyst?

The AI Fraud Analyst is an AI-powered tool that turns fraud and identity data into clear, actionable risk assessments. It analyzes specific data points submitted during a conversation, including IP addresses, email addresses, phone numbers, URLs, and transaction data.

The AI Fraud Analyst evaluates available fraud signals and explains how those signals contribute to the overall level of risk. Depending on the data provided, analysis can include signals such as IP reputation, proxy or VPN usage, email validity, phone carrier, and other indicators returned by IPQS fraud detection products.

The results may include a combined fraud score, a risk determination, an explanation of the factors that affected the assessment, and suggestions for next steps. The AI Fraud Analyst is meant to help bridge the gap between raw fraud signals and the business decisions those signals might lead to.

Use cases

The AI Fraud Analyst can support a variety of fraud prevention and investigation workflows, including:

• Identity verification: Review information such as email addresses, phone numbers, addresses, and related identity signals to identify inconsistencies or indicators of synthetic or misrepresented identities.
• Payment fraud prevention: Evaluate transaction details, billing and shipping information, card-related data, identity mismatches, and other risk signals before approving or declining a transaction.
• Bot, proxy, and anonymization detection: Analyze IP reputation and related signals for indicators such as VPN, proxy, Tor, crawler, automated, or abusive activity.
• Account takeover investigation: Review contact information, breach exposure, IP reputation, and other available signals that may indicate compromised credentials or suspicious login activity. – Entity risk analysis: Examine individual IP addresses, email addresses, phone numbers, URLs, or transaction records and get an explanation of the fraud indicators that are available for them.
• Fraud review support: Combine multiple risk signals into a summarized assessment that can help analysts determine whether activity should be allowed, reviewed more closely, challenged, or denied according to their own fraud policies.

How to use the AI Fraud Analyst

Submit the specific data you want to analyze directly into the AI Fraud Analyst conversation. This can include an individual IP address, email address, phone number, URL, transaction record, or other supported data available for fraud analysis.

To achieve the best results, it is important to include relevant context together with the data. For example, a phone number will be more meaningful when evaluated alongside the email address, IP address, transaction details, or account information associated with the same user. Fraud indicators are often more useful when assessed together rather than individually.

After the analysis is complete, review the returned risk score, verdict, detected risk factors, explanations, and any recommended actions. You should take your organization's own fraud rules, acceptable risk thresholds, and review procedures into account alongside these results.

Safety and data privacy

The AI Fraud Analyst is designed to analyze only the specific data provided for the current analysis. It uses submitted information to evaluate fraud risk and produce the requested assessment.

All AI Fraud Analyst processing is performed by IPQS's own in-house AI on IPQS servers. IPQS does not provide data submitted to the AI Fraud Analyst to third parties for any reason.

The AI Fraud Analyst does not use prior API activity or unrelated historical customer data as background context for a conversation. Previous phone validation requests, Fraud Flows sessions, transactions, or other activity are not automatically carried into a new analysis.

The tool is intended for fraud analysis, not long-term profiling. Personally identifiable information submitted for analysis is not treated as persistent knowledge beyond the immediate analysis.

As with any fraud investigation process, you should only provide data that is both suitable and properly authorized for analysis. When using the AI Fraud Analyst, organizations must still adhere to their own privacy, security, data handling, and access-control requirements.

Caveats and limitations

The AI Fraud Analyst is meant to assist with fraud decisions, not to replace the organization's fraud policies or human judgment. Keep the following limitations in mind:

• Risk assessments are probabilistic: A low-risk result does not guarantee that activity is legitimate, and a high-risk result does not guarantee fraud. False positives and false negatives are possible.
• The AI Fraud Analyst has session-bound context: It does not automatically know what data has previously been processed through other APIs, Fraud Flows, or earlier conversations. Requests for an account-wide or historical summary will be incomplete unless you supply the relevant data in the current conversation.
• Data can change over time: Reputation, carrier information, breach exposure, IP status, domain status, and other enrichment data may change. An assessment reflects the information available at the time of the analysis.
• Generated explanations should be reviewed: The AI Fraud Analyst summarizes and interprets available signals using a Large Language Model (LLM). Base important decisions on the underlying fraud data and applicable business rules, especially when the outcome affects a customer or transaction.