With the burgeoning growth of electronic financial transactions and their relative anonymity, sophisticated fraud schemes have exploded in the last 5 years, as reported by the FBI and leading publications such as The Wall Street Journal, Forbes, and the New York Times. As reported by CNBC, over 16 Billion dollars was lost to identity theft fraud alone. However, savvy CFOs are using Machine Learning to detect these fraud schemes and thwart these criminals.
We’re living in an era of extreme automation, high transaction volumes, and a highly connected world where it’s so virtually easy for fraudulent transactions to hit any business. To protect your business from fraud schemes and bad actors before they can cause significant damage, detecting fraud patterns is essential and a huge payoff when done in real-time.
Effective fraud detection and financial control initiatives leverage advanced analytics and machine learning techniques to derive valuable and actionable information for managers. Today’s enterprises churn out humongous volumes of data but are still unable to use most of that data in its raw form. The task of acquiring, cleansing, shaping, and bending the raw operational data for analytics or other business purposes is known as data preparation.
Continuous monitoring (CM) and continuous auditing (CA) systems provide real-time monitoring and reporting of business processes, activities, and transactions. This could largely benefit CEOs, COOs, and board members who are concerned with the risking risk, regulation, and costs in their organization.
With the rapid growth of the internet and the IoT and the resultant digital transformation of the world we live in, there is an explosion of data that is being generated, collected, and stored. There is data available for “normal” transactions, as well as for the fraudulent of interest to a company. If one could successfully analyze this data and gain meaningful insights and draw conclusions from it, it would be possible to use that insight for reducing the threats and risks against organizations.