To spot fraud, organizations need better fraud identity insight. Detecting those who try to deceive for gain can be a challenge, especially since everyone now has an immense and rapidly changing digital footprint. Humans are turning into living, breathing, big data machines. Because of this, organizations need to understand entities and make sense of all data points, from persistent structured data to streaming data sources such as wearable devices in the Internet of Things.
Fraud Identity Insight
Let’s take a simple example to illustrate the importance of context and understanding identities. Consider the following sentence: “I ducked as the bat flew by.” In this case, it isn’t good enough to have just the facts. Without context, this fact could be misunderstood and misused. Misunderstanding identities is exactly what leads to fraud.
In order to understand these identities, you need more facts to help you figure out what’s going on. For instance, you could say, “I ducked as the bat flew by, and I screamed as it flew into the cave.” The noun “bat” can refer to two different things: It is both a mammal and a piece of baseball equipment. Its actions can produce a wide range of outcomes, from fear to joy. Context helps you determine which meaning is the most relevant for your current situation.
The same concept can apply in the case of fraud. For example, say a credit card company sees a that credit card belonging to a Texas man was just used to rent a car in California. About 10 hours later, the card is used to buy a large, flat-screen television in Oregon. Is that fraud?
If the credit card company saw the Texas man recently redeemed his credit card reward points to book a plane ticket to California, that context might help reduce suspicion. However, if the company realized the man previously lived in the same Oregon town for many years, that there is no sales tax in Oregon and that he recently posted a message on Facebook saying, “Happy birthday, Mom! See you soon!” the situation might start to make more sense.
This context changes the understanding of the initial piece of information. This isn’t a fraudulent transaction, but rather a son buying a birthday present for his mother. Having this additional information in real time prevents the credit card company from refusing the TV purchase and upsetting a potentially valuable customer.
Fraud Identity Analytics
Fortunately, identity analytics solutions provide a technology-based advantage to outsmart fraudsters, criminals and the nefarious in spite of their attempts to threaten and defraud organizations and individuals. You can help your organization become a super sleuth and gain fraud identity insight.
Identity analytics solutions should help create an environment in which every new piece of data is instantly evaluated and put into the proper context. Best-in-class solutions have the following capabilities:
- Sequence-Neutral at Scale: Make sense of all data, with no particular ordering or sequence required.
- Context Accumulation: Make sense of all data without having to define new rules or train the system, and take advantage of self-learning, self-correcting, cognitive systems.
- Real-World-Ready: Make sense of all data across space, geography, time, language and culture.
- Privacy by Design: Make sense of all data while preserving identities and keeping sensitive data secure.
- Managed Uncertainty: Make sense of all data without the need to cleanse or govern it so all data can be used to develop the right context.
There are data clues everywhere. It is now time to spot them and shift to a proactive instead of reactive approach to fraud. By doing this, you can spend less time sifting through the evidence and more time preventing fraud and reducing risk.
In conclusion, to illustrate: “I ducked as the bat flew by, and I was happy to find that no one in the baseball park was hurt.”
World Wide Data Governance Strategist, IBM Security
Kimberly Madia is a World Wide Data Governance Strategist for the Information Management Security and Compliance solutions. She has been with IBM for twelve...