Fraud has existed for centuries. In fact, the earliest recorded fraud attempt dates back to 300 B.C.E. in Ancient Greece, when Hegestratos, a sea merchant, attempted to sink his ship to avoid paying back a loan. Since then, fraud has only become more common, as people creatively found new ways to commit fraud and other financial crimes: money laundering, Ponzi schemes, mortgage fraud, among others.
With the more recent technological advancements in computing and the internet, entirely new classes of fraud began to emerge, like wire and debit card fraud. More fraudsters began relying on tactics through software, such as social engineering, phishing and ransomware. These issues are further exacerbated by cybersecurity vulnerabilities, such as SQL injection, XSS, and buffer overflow attacks. Meanwhile, society continues to create more applications, storing increasingly more personal data online in the form of credit cards, banking information, social security numbers and passwords.
Hackers and fraudsters can use these vulnerabilities to exploit systems, and turn a profit using otherwise confidential information. For example, a large bank was hacked, exposing bank account information for thousands of users, which was then used to make hundreds of unauthorized purchases on a popular entertainment ticketing website. What’s worse, these threats don’t always come from the outside, and not all are carried out intentionally by the party that supposedly committed the fraud. By some estimates, over 50% of an organization’s data breaches and fraud are initiated by someone who works from within an organization. Which is why organizations, whether financial or sales oriented, should act to minimize these threats as soon as possible. But being able to react to fraudulent transactions is not nearly enough. Smarter developers and IT practices can help, but only get us so far. Technology changes too rapidly. Fraudsters are easily able to adapt their methods to new rules and safeguards.
Many times, the only effective solution is a self-learning system of prevention. A robust and adaptive system gives companies the best chance at fighting fraud. A system that allows one not only to be reactive to fraudulent attempts, but also to predict where fraud can and will happen. Such a system should be able to efficiently process large amounts of data, such as incoming transactions, and alert organizations when suspicious activities or anomalies surface. That system should also be able to predict when there is potential for fraudulent behavior: We’ll show you our adaptive analytics system learning from real time activity over the next four articles!
Expero’s Fraud Toolkit gives organizations the ability to do all of these things. Below is an example of our Fraud and AML toolkit designed to help learn and combat fraud in many types of organizations.
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