Corporate Fraud Insights

Report

Corporate Fraud Insights – which is one of our Financial Crime Solutions - uses advanced, self-learning and behavioural analytics to determine whether a payment is high risk.

On a daily basis, the solution analyses potentially tens of millions of transactions to identify high risk payments which fit the characteristic of these types of frauds and deliver a manageable number of alerts, precisely tuned to a customer’s preference. The alerts are rank-ordered by potential risk so
clients can prioritise investigation activities. The accuracy of the solution (built on historical client data) minimises the incidence of false positives. We alert on a tiny fraction of one percent of all transactions while still managing to detect high levels of fraud.

The solution consists of an advanced analytics model which is trained using the most current machine learning techniques. Over time, the model adapts to changing fraud behaviour via supervised learning, using new fraud and legitimate data to recognise slight alterations in fraud types. Additionally, behavioural rules can be tuned to exclude low risk payments to avoid flagging transactions which do not fit the behaviour of a fraudster.

Corporate Fraud Insights identifies:
Invoice Redirection Fraud: This fraud occurs when the beneficiary account number is different from prior history between beneficiary and payer due to a fraudster duping a business into redirecting the regular payment details to the fraudulent account;

New Relationship Fraud: This fraud occurs when there is no payment history between payer and beneficiary, i.e., the beneficiary account has not been seen before and there is no payment history with the beneficiary name. Fraudsters often do this by either intercepting the first invoice to a new supplier or by convincing finance teams to pay a bogus invoice. A key attack that can be identified with these alerts is CEO fraud, which is currently an extremely high profile and damaging type of fraud.

 

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Corporate Fraud Insights

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