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Analytics systems to help prevent money laundering, fraud

16th May 2014

By: Schalk Burger

Creamer Media Senior Deputy Editor

  

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Banks can improve the detection and prevention of potential money laundering and fraudulent transactions by using business analytics systems, says business intelligence firm SAS Analytics fraud and financial crimes regional practice lead William Lawrence.

To prevent fraud and money laundering, banks must adhere to the Financial Intelligence Centre Act No 38 of 2001 by identifying and verifying customer information, including names, identity numbers, physical addresses and income tax numbers, and must report all suspicious transactions.

However, banks often find these regulations onerous, with financial service firms Nedbank, First Rand and Absa acknowledging that there are flaws in their systems that are designed to correctly capture customer information and identification and detect suspicious transactions.

The South African Reserve Bank (SARB) fined the four largest banks in South Africa a total of R125-million for not keeping adequate customer verification details and transactional records, and not focusing enough on legislative regulations and controls to detect and prevent such transactions. The banks were ordered to take remedial action.

However, banks can use analytics systems to comply with governance, risk and compliance regulations quickly and take immediate action, which was an instruction of the SARB.

Anti-money laundering (AML) and counterterrorism analytics solutions adopt a risk-based approach to AML. The programs flag unusual behaviour, trace large, complex transactions and identify funds’ sources over extended periods, says Lawrence.

Through data mining, analytics employs multiple detection methods to monitor more risks in large data volumes and ensure compliance with regulations. Analytics can process more than two-billion transactions in 12 hours and can flag suspicious activity within seconds.

The software will check for multiple risks during a single data pass, which can be customised to a client’s needs. A bank, for example, may want to check the origin of the funds, the identity of the person initiating the transaction, unusual behaviour and suspicious actions.

The system will also highlight links to others who may be involved in the dubious activity and may be part of organised crime syndicates. It will score these alerts before passing them on to investigators, which enables the investigators to more accurately identify actions and relationships that present the greatest risk, adds Lawrence.

“By implementing an AML solution, a top US bank reaped significant benefits, including reducing processing time from 18 hours to about four hours, while increasing the number of scenarios deployed to monitor emerging risks. The bank also managed to increase the number of historical days during which activity could be monitored and increased the number of nightly transactions.”

Verifying customer information is becoming more crucial because criminals are becoming more sophisticated at concealing the proceeds of crime using false identities and documents to hide the origin and ownership of funds, concludes Lawrence.

Edited by Martin Zhuwakinyu
Creamer Media Senior Deputy Editor

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