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.
Reimbursable employee expenses are a significant cost for most businesses, and unfortunately, they are often a source of fraud by unscrupulous employees. To combat this, businesses typically rely on auditing employee expense reports manually. As this is a tedious and very labor-intensive process, usually only 10% of all the receipts get audited. Thus businesses have an increased risk of expense report fraud going undetected.
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.
As the volume of online transactions continues to grow exponentially, there is a new quest for enterprises to start new channels of business, gain more online presence, retain existing revenue streams, and stay relevant to current technology. Many organizations are unable to detect patterns related to Fintech fraud because they are stuck doing traditional rule-based approaches. Emerging modern technologies, like NEMESIS, use the power of AI and machine learning to fight against financial fraud.