The Audit Committee Forum discussion on 21 Jan 2016 raised some interesting discussion on the use of data analytics in fraud detection and prevention. Though it sounded new, the corporate world in America has been using Big Data for many wonderful things thanks to the tech companies developing tools to analyze their business trends and to add value. There are many articles written on how Obama used big data to rally voters and win his second term. If anyone is interested in the use of big data, the New York Times bestseller “BIG DATA – A Revolution that will transform how we live, work and think” book written by Viktor Mayer- Schönberger and Kenneth Cukier gives a fascinating insight on this subject.
Big data is about seeing and understanding the correlation between the pieces of data to produce information that is useful. Any discussion on frauds and prevention does not go without a discussion about data and analytics. This is also due to the advancement in technology and its use for online transactions, the volume of transactions, use of cloud storage and of course complexity in anything corporates do today!
The use of data analytics allows for significant efficiency in automated systems to monitor fraudulent or suspicious behaviour. Using technology for testing of transactions could be extended to 100% of the population and multiple angles can be tested at any time. Data analytics can design the tests to identify potentially fraudulent behaviours to prevent frauds.
However, we have significant challenges in the local context to take this beyond ‘just’ discussions. The techniques cannot be used without good quality data that has been collected, validated and cleansed. Therefore, a data warehouse type of central repository for data has to be created to enable data analysis. The infrastructure also should be able to handle the volume of data that is required for the analysis. This requires investment and many CEOs cannot see the benefits prior to approving such an investment. Further, the skill sets required to perform analysis and interpret the results that is meaningful, has to be acquired, developed and retained, which is scarce and difficult, in addition to being costly.
Internal audit departments should canvass for investment in data analytics tools and techniques because it’s a great way to detect the first signs of fraud and prevent incipient losses from growing. They should design a data analytics process that clearly identifies:
Relevant data to collect.
Intervals and timings to obtain the data.
Integration of the process into a fraud risk assessment program.
Tools and techniques required.
Methods to interpret the data to assist in detecting and preventing fraud.
Continuous monitoring and reporting processes.