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.
Jurisdiction Derivation, Powered by AI, Helps Financial Institutions Reduce Risk and Their Number of AML Investigations
Financial institutions are held accountable by regulators to ensure they are taking a risk-based approach in their AML/BSA compliance operations. As such, institutions must consider AML risk based on certain types of customers and transactions, including risky jurisdictions impacted by political or economic unrest.
The AI-powered QuantaVerse Automated Volume and Value (V&V) Transaction Analysis solution provides risk managers with better insights into variances in account activity that might indicate risks of financial crimes, or that suggest an account is being used for something other than its stated purpose. Analysis of this nature is a growing regulatory burden driven by the expectation that FIs understand the risk profile of clients as well as their clients’ clients.
CASE STUDY: How QuantaVerse’s AI Tech Helped a Forward-Thinking Commercial Bank Cut Costs While Reducing False Positives
Financial institutions have for years banked on rules-based transaction monitoring systems (TMS) to root out money laundering and other financial crimes, only to be served up copious false positives that result in paralyzing inefficiencies, runaway investigation costs, and unseen false negatives that represent serious risk to the institution.