Using data along with other cutting-edge tools can help organizations make better decisions and step up efforts to monitor fraudulent transactions.
Forty years ago, banking fraud might have involved simply forging an account holder’s signature on a withdrawal slip. Now the speed and intricacy of the schemes are mind-boggling: a student bank account (with details obtained by a crime gang) receives a payment of £10,000. Within minutes, the funds have been cycled through dozens of accounts before being forwarded to an international account, where the trail suddenly goes cold. No alarm bells go off. No inquiries are made to the bank. The fraud is only discovered much later, at which point the money and the fraudsters are long gone.
Around the world, fraud is an ever-increasing risk for businesses of all stripes. The 2015/16 Global fraud report by Kroll and the Economist Intelligence Unit found that 75 percent of companies surveyed had been victims of fraud in the past year, an increase of 14 percentage points from three years earlier. And, perhaps unsurprisingly, fraud is a particularly serious issue for financial institutions. The Association for Financial Professionals’ 2016 Payments Fraud and Control Survey found that 73 percent of finance professionals reported an attempted or actual payments fraud in 2015.
As prevalent as the fraud problem is for financial institutions, it can be difficult to address. Factors that contribute to the challenge include the sheer volume of transactions handled by most institutions versus the relatively small number of fraudulent transactions, the speed with which technology allows fraudsters to operate, poor or incomplete data, and the lack of information sharing among financial institutions. All too often, banks lack the technology and capabilities to implement the necessary safeguards, responding to a primarily digital problem in an analog way—for example, phone calls attempting to piece together the path of a rapid series of money transfers.
For financial institutions, data and analytics can speed the decision cycles used to observe, orient, decide, and act in fighting fraud. Since the best insights are often at the margins of where industries or data sets overlap, it’s necessary to pose targeted questions and develop solutions from a variety of information sources. By combining proprietary data sets with industry benchmarks and government information, financial institutions can use artificial intelligence, Machine Learning , and analytics in the fight against financial fraud. Financial executives should move now to adopt appropriate processes, develop and acquire the necessary talent, and create the right culture to integrate analytics into their fraud-detection efforts.
Defining the role of analytics in addressing the challenges of financial fraud
A vast amount of data flows through financial-services organizations, so the ability to harness those data and analyze them effectively could transform the industry’s fraud-detection efforts and provide a host of other benefits. Coupling these rich data sets with appropriate analytical models provides a way to harvest the information needed to identify and prevent fraud more effectively. In some cases, an institution’s data can be combined with other fraud markers necessary to provide a data set for training the analytics models used to detect possible incidents of fraud.
For financial institutions and government agencies looking to fight fraud, then, the goal should be to aggregate the existing data needed to support more timely detection and to couple those data with the expertise needed to create and apply the most effective fraud-detection models.
