Business function
The most documented AI use cases for fraud prevention are claims fraud detection, real time fraud scoring and chargeback and representment, across 116 public deployments by 107 organizations. Detecting, stopping and investigating fraud and scams against customers and the organization.
Ranked by the number of organizations with a public deployment, as of 11 November 2026.
Claims fraud detection is AI that scores every insurance claim for fraud from first notice of loss onwards, combining claim, policy, document, image and network data.
Real time fraud scoring is machine learning that decides in milliseconds how likely each card authorization and account to account payment is to be fraudulent, so a bank can act.
AI chargeback and representment is AI that runs the dispute engine room for issuers, acquirers and merchants, mapping each dispute to the network reason code.
AI order fraud screening decides, at the moment an online order is placed, whether to accept it, hold it for review or decline it, for an online retailer or marketplace.
Application and identity fraud detection is AI that checks incoming account and loan applications for banks and lenders to catch forgery and fraud rings.
AI merchant underwriting and risk monitoring helps acquirers, payment facilitators and software platforms with embedded payments decide which merchants to accept and on what terms, and watch for risk afterward.
Scam payment interception is AI that talks to a customer who is about to authorize an instant payment that looks like a scam.
AI fraud alert confirmation is a customer facing AI agent that contacts a cardholder when a fraud engine flags a card transaction, verifies them and asks if they made it.
AI customs risk targeting and container selection is machine learning that scores every import or export declaration for the risk it carries contraband, is misclassified or is undervalued.
AI mule network detection uses graph and behavioral machine learning to find money mule accounts and the networks around them, for investigators.
107 organizations in 28 countries run these use cases in public. 66% of those 116 deployments disclose a measurable outcome.
Card dispute intake is a customer facing AI agent that finds a disputed card transaction and separates suspected fraud from merchant disputes and simple confusion. It explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.
AI fraud alert confirmation is a customer facing AI agent that contacts a cardholder when a fraud engine flags a card transaction, verifies them and asks if they made it. It reaches them in the channel they actually respond to and acts on the answer: releasing the block so a retry succeeds, or freezing the card and starting the fraud claim.
AI fraud alert triage is an AI agent that works a fraud alert queue behind the scenes as an analyst's first pass, without contacting the customer. It enriches each alert with customer, device and payment context, closes clear false positives under documented rules, merges duplicates, and routes genuine risk to an analyst with a drafted rationale.
Scam text message blocking is AI in the operator's messaging network that scores text messages as they pass through and stops phishing and scam messages before they reach the subscriber. It looks at sender behavior, message volume and links, and it is separate from labeling scam calls and from fraud against the operator itself.
Application and identity fraud detection is AI that checks incoming account and loan applications for banks and lenders to catch forgery and fraud rings. It analyzes documents, device and application data across the whole queue and cross checks against bureau and official sources to flag forged or AI generated documents, synthetic and stolen identities, and coordinated rings.
Benefit fraud and error detection uses risk models that help a social security or benefits agency decide which claims, payments and recipients to check for fraud or error. Caseworkers then verify the riskiest cases first, while every decision on entitlement stays with a person and the model is tested for fairness before and during use.
AI chargeback and representment is AI that runs the dispute engine room for issuers, acquirers and merchants, mapping each dispute to the network reason code. It gathers the matching evidence, assembles a network compliant chargeback or representment package, drafts the rebuttal, tracks every deadline and processes pre dispute alerts so a refund can be issued before a chargeback lands.
AI customs risk targeting and container selection is machine learning that scores every import or export declaration for the risk it carries contraband, is misclassified or is undervalued. A customs administration sends physical inspection, scanning and detailed review only to the small share of containers, vehicles and parcels that need it; the rest clear without an officer touching them, and an officer reviews and acts on every high risk alert.
AI for healthcare claims fraud, waste and abuse detection reads healthcare claims and provider billing patterns to flag fraud, waste and abuse for a payment integrity investigator. It checks claims before or shortly after payment and sends every flag with its reasons to the investigator, while a narrow set of clear rule violations can be denied automatically before payment.
Claims fraud detection is AI that scores every insurance claim for fraud from first notice of loss onwards, combining claim, policy, document, image and network data. It finds suspicious claims, organized rings and inflated losses, and sends each alert with its reasons to a claims handler or special investigations unit for review.
AI merchant underwriting and risk monitoring helps acquirers, payment facilitators and software platforms with embedded payments decide which merchants to accept and on what terms, and watch for risk afterward. It checks what a business really sells and how risky it is at onboarding, then watches every active merchant for changes in behavior, ranking the few that need an analyst so fraud, prohibited trade and credit losses are caught early.
AI mule network detection uses graph and behavioral machine learning to find money mule accounts and the networks around them, for investigators. These include circular flows, layering chains and clusters of newly linked accounts, and the AI supports investigators in tracing scam proceeds and restricting accounts before the money is gone.
Tax compliance risk scoring is AI that scores tax returns, taxpayers and transactions for the risk of error, underreporting or fraud, for a tax administration. It directs audit and compliance capacity to the highest risk cases, while an officer decides every compliance action and the selection itself is monitored for fairness.
Telecom fraud detection is AI that protects an operator's network, revenue and numbers from fraud by watching call, messaging, roaming and account activity. It detects SIM swap and port out takeovers, international revenue share fraud (IRSF) and Wangiri one ring scams, blocks or flags them in real time, and shares risk signals with banks and other businesses that rely on the phone number for security. Scam calls aimed at subscribers are handled by call blocking.
AI loss prevention at self checkout uses cameras and computer vision to spot items that were not scanned and to prompt the shopper to fix them. Some retailers also check the cart against the receipt at the exit, leaving final checks to staff.
AI order fraud screening decides, at the moment an online order is placed, whether to accept it, hold it for review or decline it, for an online retailer or marketplace. It draws on the buyer's device, behavior, identity and order history, so genuine orders are approved quickly and fraudulent ones are stopped before they ship, often backed by a vendor guarantee against the chargebacks that slip through.
Scam payment interception is AI that talks to a customer who is about to authorize an instant payment that looks like a scam. It combines the payee check and the risk score, asks targeted questions about the payment in plain language, explains the specific scam pattern, and holds, delays or escalates the payment to a human specialist when the risk stays high. Unlike fraud scoring, which stops payments the customer did not make, it protects customers from payments they are being manipulated into making.
Public tender risk screening is AI that reads tender notices and procurement records and alerts auditors to likely errors, rigging or fraud before a contract is awarded. Audit offices and ministries use it to give auditors a short list of flagged tenders to review while a tender is still open.
Spam and scam call blocking is AI in the operator's network that protects subscribers from unwanted calls by analyzing incoming calls in real time. It blocks known fraudulent calls and labels suspected scam, spam and spoofed calls on the customer's screen before they answer, so subscribers can decide whether to pick up. Fraud against the operator itself, such as SIM swap or revenue share fraud, is a separate use case.
Real time fraud scoring is machine learning that decides in milliseconds how likely each card authorization and account to account payment is to be fraudulent, so a bank can act. It works without any conversation, combining behavioral, device and network signals, so the bank can approve, challenge or block a payment before the money leaves. Working the resulting alerts and talking to the customer about them are separate use cases.
Computed from the 116 public deployments behind these use cases. Numbers update with every checked record.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Fraud loss reduction | 40% | 30% to 82% | 5 | 4 organization, 1 vendor |
| False positive reduction | 50% | 40% to 75% | 4 | 1 organization, 3 vendor |
| Detection improvement | 135% | 30% to 300% | 3 | 1 organization, 2 vendor |
