Bank Albilad scores every transaction in real time to hunt mule accounts
The Saudi bank uses machine learning and network analysis from SAS. The vendor reports half the false positives and 70% more fraud losses prevented.
Bank Albilad, a Riyadh-based Islamic bank founded in 2004, rebuilt its fraud management on SAS Fraud Management and SAS Viya Visual Investigator to meet the Saudi Central Bank’s fraud requirements (SAS).
What it does
The system scores transactions and newly opened digital accounts in real time using machine learning models, and looks for fraud at both transaction and account level. Investigators use network diagrams to find communities of mule accounts by analysing customer relationships. SAS says Bank Albilad was the first Saudi bank to use this kind of ad-hoc network analysis.
The reported results
According to SAS:
- 100% of transactions are assessed in real time.
- 100% of newly opened digital accounts are scored in real time.
- False positives fell by 50%.
- Fraud loss prevention rose by 70%.
- 98% of transactions are approved within seconds.
- Investigators became 30% more efficient.
Read with care
These are vendor-published figures from a customer story, and SAS notes that results vary by customer. They have not been independently verified. The pattern is still useful: the measurable gain is fewer false alarms, which means fewer good customers blocked.
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