Business function
Detecting, stopping and investigating fraud and scams against customers and the organization.
43 organizations in 11 countries run these use cases in public. 68% of those 50 deployments disclose a measurable outcome.
A customer facing AI agent that handles the "I do not recognise this charge" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, 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.
A customer facing AI agent that contacts the cardholder as soon as the fraud engine flags a card transaction, in the channel they actually respond to, verifies them, asks whether they made the transaction and acts on the answer: releasing the block so a retry succeeds, or freezing the card and starting the fraud claim.
An AI agent that works the fraud alert queue behind the scenes as the 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.
AI that checks incoming account and loan applications for forged or AI generated documents, synthetic and stolen identities, and coordinated application rings, by analysing documents, device and application data across the whole queue and cross checking against bureau and official sources.
Risk models that help a social security or benefits agency decide which claims, payments and recipients to check for fraud or error, so that caseworkers 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 that runs the dispute engine room for issuers, acquirers and merchants: it maps each dispute to the network reason code, 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 that scores every insurance claim for fraud from first notice of loss onwards, combining claim, policy, document, image and network data to find suspicious claims, organised rings and inflated losses, and sends each alert with its reasons to a claims handler or special investigations unit for review.
AI that helps acquirers, payment facilitators and software platforms with embedded payments decide which merchants to accept and on what terms, by checking what a business really sells and how risky it is at onboarding, and then watches every active merchant for changes in behaviour, ranking the few that need an analyst so fraud, prohibited trade and credit losses are caught early.
Graph and behavioural machine learning that finds money mule accounts and the networks around them, such as circular flows, layering chains and clusters of newly linked accounts, and supports investigators in tracing scam proceeds and restricting accounts before the money is gone.
Models that score tax returns, taxpayers and transactions for the risk of error, underreporting or fraud, so that a tax administration spends its audit and compliance capacity where the risk is highest, with an officer deciding every compliance action and the selection itself monitored for fairness.
AI that protects the operator's own network, revenue and numbers from fraud: it watches call, messaging, roaming and account activity to detect 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 that talks to the customer when they are about to authorise 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.
AI in the operator's network that protects subscribers from unwanted calls: it analyses incoming calls in real time, 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.
Machine learning that decides in milliseconds, without any conversation, how likely each card authorization and account to account payment is to be fraudulent, combining behavioural, 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 50 public deployments behind these use cases. Numbers update with every checked record.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Detection improvement | 135% | 30% to 300% | 3 | 1 organization, 2 vendor |
| False positive reduction | 50% | 40% to 75% | 3 | 1 organization, 2 vendor |
| Fraud loss reduction | 30% | 30% to 76% | 3 | 3 organization |
ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia), AXA Switzerland, Airwallex, Assurant, BCU, BT Group, Belastingdienst, Bell Canada, BigPay, CNG Holdings, Capital One, Centers for Medicare and Medicaid Services, Close Brothers Motor Finance, Coast, Commonwealth Bank of Australia, Department for Work and Pensions, Gemeente Rotterdam, General Insurance Association of Singapore, GitHub, HM Revenue and Customs, Internal Revenue Service, Klarna, Lemonade, Macquarie Bank, Mastercard, NatWest Group, Pay.UK, Payoneer, Reserve Bank Innovation Hub (Reserve Bank of India), Revolut, SEB, Starling Bank, Stripe, Tekmetric, Telstra, Tokio Marine & Nichido Fire Insurance, U.S. Department of the Treasury, Bureau of the Fiscal Service, Uitvoeringsinstituut Werknemersverzekeringen (UWV), Virgin Media O2, Visa, Vodafone, Weave Communications, Westpac.
