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.
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.
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.
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.