Writing
A series of articles on fraud detection, assurance over machine-learning systems, and the governance gaps that organised schemes exploit. These are in preparation; I will link each one here when it is published.
Business Email Compromise in Insurance Claims Payments
A payment-diversion scheme in which the compromise happened outside the insurer altogether — at the external loss adjuster, the least defended link in the chain — and every payment control applicable to the settlement operated exactly as designed without preventing the loss.
Collusion at the Point of Settlement
What prosecuted vendor-adjuster schemes show: segregation of duties was secured in every case, and was ineffective. Detection came from analysing relationships across transactions, or from someone outside the institution picking up the phone.
From Sampling to Signals
Sampling and anomaly detection answer different questions. Fraud is not distributed at random — the perpetrator picks the transaction precisely because it is unlikely to be picked by the auditor.
Auditing the Algorithm
Eight control objectives for a deployed machine-learning fraud model. The exposures are governance exposures: where the training data came from, how the labels were derived, who may move the threshold, and whether the institution can state the basis on which it acted.
The Three Lines Against Organised Fraud Rings
Organised schemes do not defeat the three lines model; they exploit a gap in the allocation it prescribes. The pattern is visible from no position the model defines.