Fraud

Catch the applicants who never intend to pay

First party fraud hides inside your first payment defaults. Infact screens every application in real time across every lender reporting into our bureau, flagging data manipulation and intent signals at the point of application.

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At a glance

Detect first party fraud at the point of application.

Income, employment and household details that change across applications are read as what they are: intent.

Explain your first payment defaults.

Outcome intelligence from across the bureau links an application to confirmed fraud and previous first payment defaults on the same identity.

Fraud savings without the friction.

Real-time screening with no footprint on the consumer's file, no added journey steps, and fewer false positive referrals holding up genuine customers.

The pain

The first payment defaults nobody can explain.

"We tested every bureau variable we could get. The answer on all 850 came back no."

Credit Risk Lead, UK Consumer Lender

The answer was never in the file. It was in behaviour the file never captured.

No repayment is fraud.

"No repayment is fraud. If the customer made a repayment, then it's credit."

Credit Risk Lead, UK Consumer Lender

Most lenders book all of it as credit loss. That means the fraud has no owner, no budget and no fix.

The proof of concepts that prove nothing.

"We could see they were synthetic IDs. Those cases were in the proof of concept data, and they came out as low risk."

Application Fraud Lead, UK Card Issuer

If a test cannot find the fraud you already confirmed, it cannot size the fraud you have not.

How it works: intent shows before the first payment is missed

Tuesday: a credit card application.
Wednesday: a different lender, two dependants instead of three.
Thursday: one dependant, a better job title, higher income.
Three clean applications. One invisible sequence.

Screening at the point of application

Every application is screened in real time against activity across every lender reporting into the Infact bureau. Detection focuses on data manipulation between applications: income that creeps up, employment status that changes with each attempt, household composition that shifts, amounts that escalate. Shopping around for credit is healthy behaviour and is treated as a positive signal. Manipulation is not.

Outcome intelligence

Because Infact is a bureau, every application resolves to a single verified identity, matched even as names, addresses and contact details shift. One person, one view, however the story is told. Confirmed outcomes flow back into the bureau. An application can be linked to previous first payment defaults and confirmed fraud on the same identity, and detection strategies learn from outcomes as they mature.

Third party signal, and growing

Identity, email, phone, IP and mobile network intelligence (SIM swap recency, call forwarding, number tenure, direct from the network operators) extend detection to third party fraud and account takeover. These signals prove themselves in live testing, which is exactly how we deploy them: shadow mode on your applications, measured against your confirmed outcomes.

Walk through API components

The fraud endpoint allows you to retrieve an individual’s real-time credit exposure and future payment schedules.

verifying identities

Monitor how users enter PII and catch behaviours consistent with bad actors

credit hunger

See application velocity across the network

network and email risk

Detect the use of VPN, IP geolocation, and age and validity of email address

phone risk

Check if numbers are new, disposable or higher risk

Request | Affordability

{
"title": "Miss",
"firstName": "Isobel",
"middleName": "Mary",
"lastName": "Tucker",
"email": "isobelt99@example.com",
"dob": "1994-01-21",
"phone": "447402123456",
"ipAddress": "92.15.92.213",
"address": {
"buildingNumber": "5",
"line1": "5 Moorside View",
"line2": "Station Street",
"city": "Glossop",
"postalCode": "SK13 8BD",
"countryCode":"GB"
}
}
}
"summaryRiskScore": 0.4,
"appplicationVelocityRisk": {
"score": 0.6,
"ipCountSameDay": 3,
"postalCodeCountSameDay": 2,
"addressCountSameDay": 2,
"phoneCountSameDay": 2
},
"ipRisk": {
"score": 0.4,
"isVpn": false,
"milesFromApplication": 203,
"city": "London",
"provider":"TalkTalk",
},
"emailRisk": {
"score": 0.2,
"isValidDns": true,
"isDisposable": false,
"isDeliverable": true,
"breachCount": 3,
"earliestBreachDate": "2008-07-22",
},
"addressRisk": {
"score": 0.2,
"postalCodeMatch": 0.2,
"registerMatch": 0.82,
},
"phoneRisk": {
"score": 0.3,
"isValidNumber": true,
"isRoaming": false,
"ownershipMatch": 0.9,
"nameMatch": 0.8,
"addressMatch": 0.7,
"postalCodeScore": 0.6,
"dobMatch": 0.6,
"simSwap": 0.7,
"provider": "Vodafone",
"countryCode":"GB"
}
}

How we prove it

We prove the detection rate on your own book before any commercial conversation. A data-led exercise screens your historic applications against your confirmed fraud outcomes and shows exactly what would have been caught, what would have been referred, and the false positive cost of each detection strategy. Where a signal only proves itself live, such as SIM swap, we run in shadow mode and measure against outcomes as they mature. No integration required to start. Pricing follows the demonstrated value, per API call.

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