DATA PRIVACY & FRAUD
Two of the biggest problems facing the financial industry today are data security and fraud. Breaches are happening left and right, and millions of real people have their identities compromised with each leak. It’s nearly impossible to close all the backdoors that exist, and hackers keep getting craftier with their attacks. What if, in addition to doubling down on closing those backdoors, financial institutions could create honeypots of artificial, synthetic data to fool would-be hackers into stealing data that looks eerily real, but isn’t? This would create a deterrent dividend – potential hackers would be afraid of detection.
Better yet, what if an institution could synthesize all of its data in such a way that the important information was retained without containing personally identifiable information, and this could be done prior to sharing it internally? This data becomes much less valuable to a hacker, and simultaneously becomes much less of a risk for the institution to handle on less secure channels.
With the average cost of fraud rising year over year, companies are scrambling to find methods that allow them to detect questionable activity earlier and more effectively. Fraud comes in many forms – money laundering, credit card applications, loan applications. Financial institutions process thousands of transactions every day, which is far too many for a human to look over and identify fraudulent activity by eye.
Not to mention that fraudsters are getting more creative. Anomalies are very rarely obvious enough to notice by eye anymore. Most perpetrators are getting sneaky and making their transactions look similar to other, nonfraudulent ones. Without the presence of an obvious outlier, how can financial institutions know that a transaction is suspicious?
