Connect with us

People 'n' Issues

Analytics can save banks

Published

on

Recently, the Reserve Bank slapped South Africa’s four big banks with penalties for not implementing regulations to control money laundering and combat the financing of terrorism. However, this could have been avoided through the correct use of data mining and analytics, writes WILLIAM LAWRENCE.

To be clear, the banks were not accused of facilitating transactions involving money laundering and terrorism finance. Rather, they were penalised for not placing enough focus on legislation and controls that detect and prevent such transactions. An example is the Financial Intelligence Centre Act (FICA), 38 of 2001, which aims to prevent money laundering by insisting that banks identify and verify customer information. This includes their names, ID numbers, physical addresses and income tax numbers. Banks are required to keep records of this information for at least five years, to report suspicious transactions, and to implement internal rules, such as staff training, to ensure FICA requirements are met.

It seems FICA was a key consideration in the Reserve Bank’s ruling. Banks were penalised for not keeping adequate customer verification details and transactional records, and were ordered to take remedial action. And they admitted to weaknesses. According to a report in Business Day, on 17 April, Nedbank, FirstRand and Absa acknowledged flaws in systems designed to ensure correct capture of customer information and identification of suspicious transactions.

While all financial institutions will no doubt have had initiatives in place, the Reserve Bank’s instruction to take immediate remedial action to address deficiencies in managing and processing suspicious and unusual transactions, will require all banks to take a new look at solving these deficiencies quickly.

Financial institutions are among the most heavily regulated – and for good reason. Although inexcusable, focus is bound to unintentionally lapse in some areas. With analytics, however, banks don’t have to worry about falling foul of governance, risk and compliance laws.

Anti-money laundering (AML) and counter-terrorism analytics solutions adopt a risk-based approach to AML. They flag unusual behaviour, trace large, complex transactions, and identify funds sources over extended periods of time. Through data mining, analytics employs multiple detection methods to monitor more risks in large data volumes, ensuring compliance with related regulations. Analytics solutions can process more than two billion transactions in one night, and can flag suspicious activity in seconds – not hours.

In a single pass of data, the software will check for multiple risks, which can be customised to a client’s unique 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 rings. It will score these alerts before passing them on to investigators, which allows them to more accurately identify actions and relationships that present the greatest risk.

In fact, by implementing an AML solution, one of the top banks in the US saw significant benefits including a reduction in processing time from 18 hours to around four hours while increasing the number of scenarios deployed to monitor emerging risks. The bank also managed to increase the number of historical days that activity could be monitored and increased the number of nightly transactions.

According to KPMG, between $500 billion and $1 trillion is laundered worldwide annually. Criminals are becoming more sophisticated when it comes to concealing these proceeds of crime. They use false identities and documents to hide the origin and ownership of the funds – this makes verification of customer information even more crucial.

Money laundering has serious economic and social side effects. Failure to detect these transactions results in tax income shortfalls and higher crime rates. Undetected drug funds means more drugs on the streets and more associated violence – not to mention the funding of terrorist organisations and drug and human smuggling rings. The ability to launder money to avoid detection and prosecution is what keeps criminals in business. For them, successful money laundering means crime does indeed pay. It is only through the vigilance of financial institutions that this can be prevented and the system – and, ultimately, society – can be protected.

* William Lawrence, Regional Practice Lead: Fraud and Financial Crimes at SAS

* Follow Gadget on Twitter on @GadgetZA

Trending

Gadget