Organizations of all sizes, including financial institutions and corporations, must surmount various challenges and risks as a natural course of business, with fraud being one of the biggest. According to a 2016 global fraud study by the ACFE,the average organization loses an estimated five percent of its annual revenue to fraud. When this estimate is applied to the Gross World Product of $74.16 trillion, projected potential fraud loss totals a staggering $3.7 trillion worldwide.
Fraud committed against organizations can be carried out internally (rogue employees/management) or externally (customers, vendors or hardened criminals). Internal fraud is defined as “the use of one’s occupation for personal enrichment through the deliberate misuse or misapplication of the organization’s resources or assets.”
External fraud committed against a company can include a number of scenarios such as deceitful vendors that bill a company for goods/services that were never provided, or customers submitting bad checks or falsified account information for payments.
Some specific examples of fraud include:
- Financial statement fraud is a deliberate misrepresentation of the financial condition of an organization by misstating or omitting information in an effort to deceive. This type of accounting fraud accounts for approximately 10 percent of incidents concerning white collar crime.
- Asset misappropriation can occur when employees, vendors or customers deceive an organization by stealing or misusing an organization’s resources. A scenario of this type of fraud can occur when an organization pays for something it shouldn’t pay for or pays too much for purchased goods or services.
- Inventory shrinkage results when there is an excess in the amount of inventory listed in accounting records, but no longer exists in the actual inventory. This type of fraud, which accounts for a $60 billion annual loss to retailers, is largely the result of shoplifters and employee theft.
While organizations can’t stop all fraud, it’s essential that their internal audit and anti-fraud teams proactively protect against these destructive acts by, among other things, conducting on-going audits of company data from core accounting systems and other financial systems.
However, as criminals become more advanced and adjust their techniques, identifying fraud can become increasingly difficult. Technology can help tremendously, but legacy data surveillance solutions are unable deliver the insights needed to effectively uncover suspicious activities and, ultimately, identify new types of fraudulent activity.
Advancements in data analytics and artificial intelligence (AI) provide organizations with the ability to efficiently address multiple and complex fraud cases. When it comes to uncovering fraud, AI is particularly useful in identifying complex patterns and anomalies hidden in the data. By employing a set of AI agents to query a firm’s core accounting/finance system, travel and expense reporting system, trade finance data, third-party vendor lists, and internal e-mail systems, AI and machine learning solutions can detect and report activities that are anomalous to typical patterns indicating the possibility of fraudulent activity whether occurring internally or externally.
Today’s advanced data analytics and techniques, such as Benford Analysis, Natural Language Processing (NLP), graph search algorithms and predictive analytics, can enhance the effectiveness and efficiency of fraud detection programs.
Corporations of all sizes and types should ensure that they have strong internal audit and anti-fraud programs in place to protect their organization from financial, legal and reputational risks associated with fraudulent activity. Through the utilization of advanced data analytics and AI, organizations can improve the way they protect themselves from would-be criminals and how they conduct business.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?