AI use case · Is your data ready for fraud detection?Fraud hides in the gaps. So do your data errors.
Fraud detection tools look for the unusual: a new supplier, a changed bank account, an approval out of pattern. Messy data is unusual too. When suppliers are duplicated, approvers have left and past incidents were never recorded, the model can’t tell fraud from a typo, and the team drowns in alerts.
Starts with a 30-minute call. Commits you to nothing.
What it needs
What fraud detection needs from your data.
Each requirement, the systems it lives in, and what we check. The AI is the easy part; this list is the hard part. Each check feeds the Truth Score™, a score out of 100 for how far your data can be trusted.
Truth Score pillars that gate it mostAccuracyLineageGovernanceThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where fraud detection breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
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HR ↔ Finance
Payments approved by people who have left
What it does to the AIThe control the model assumes is in place isn’t there.
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Payroll ↔ Finance
Supplier bank details that match an employee’s
What it does to the AIA known fraud pattern, and also a common data-entry error. You need to know which.
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Bank ↔ Finance
Payments in the bank with no matching bill
What it does to the AIThe model can’t judge what it can’t see.
Gaps to test for this use case, not client findings.
Before you sign
Questions to ask the vendor selling it.
Four questions that separate a demo from a deployment. Bring the answers, or the proposal, to the Truth Score Read and we test them against your records.
- Q1
What false-alarm rate should we expect on data like ours, and who reviews the alerts?
An alert nobody has time to review is not a control. Ask for the review workload, not just the detection rate.
- Q2
Do you need recorded past cases? How many?
Supervised models need labelled examples. If you have none, ask what the tool does instead, and how it is tested.
- Q3
How do you explain an alert to an auditor or a regulator?
If the reason can’t be traced to records, the alert can’t be defended, or acted on.
- Q4
What happens to alerts nobody clears?
Uncleared alerts are a liability: proof you were warned and didn’t act.
How a Truth Score Read tests it
Fraud detection, tested against your own records.
Our engine matches the customers, people, deals and invoices across the exports from the systems behind the decision. Every red flag is checked by hand against sample records before sign-off, and the verdict comes in writing. Here is what that means for this use case.
- 01
Clean the counterparties
Supplier and customer masters checked for near-duplicates and bank detail changes.
- 02
Cross-check people
Approvers and supplier records compared with the HR and payroll exports: leavers, and overlaps between employees and suppliers.
- 03
Test the history
Payment history for gaps, back-dating and payments with no matching bill.
- 04
Give the verdict
Whether detection can run now, and which gaps would turn it into a false-alarm machine.
Two systems are enough to start. We send the exact field list after the call.
How the Truth Score Read worksWhat you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Questions
Fraud detection, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callWhy does fraud detection need such accurate data?
Because fraud models hunt for anomalies, and data errors are anomalies too. Duplicate suppliers, wrong dates and stale approvers all look suspicious. The cleaner the base, the more an alert means.
Can we detect fraud without a history of confirmed cases?
Yes, with rules and anomaly detection, but expect more false alarms and plan who will review them. Recording confirmed cases from now on is one of the cheapest improvements you can make.
Is this about customer fraud or supplier fraud?
Both follow the same logic. For many mid-market companies, payments and suppliers are where exposure sits; for lenders, payments firms and retailers, customer transactions matter too. The checks change; the method doesn’t.
Do you investigate fraud?
No. We test whether your data can support a fraud detection tool. If our checks surface something that looks like fraud rather than an error, we tell you, and you decide who investigates.
Is your data ready for fraud detection? Find out before you sign.
Exports from two or more systems in. A signed verdict on this use case within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.