AI Is Driving Fraud Prevention, But Humans Must Keep Steering
The fraud prevention highway of today looks quite different than the fraud prevention highway of even just a few years ago. Major advances in artificial intelligence and newfound affordability of machine learning techniques have paved a new road, and self-evolving fraud prevention solutions are on the way to making traditional rule-based solutions close to obsolete. Enterprises all over the world are scrambling to implement machine learning technology into their business plans, with 60% of executives reporting that AI is critical to their future success. And this is with good reason, especially in the fraud prevention sphere.
Modern criminals are not only well-financed and savvy, they’re often able to trump legacy anti-fraud programs with matching technology. Not only that, fraudsters frequently invent new illegal methods that rule-based programs can’t always recognize. Enter machine learning. Fraud prevention solutions that incorporate AI technology are able to outwit fraudsters, because they delve deep into a trove of historical data and customer patterns to instantaneously decipher fraudulent activity — whether the activity consists of methods already known to fraud prevention managers or if it consists of brand new tactics fraudsters are using.
The Human Touch: AI Can’t Perform Well Without You
However, as intelligent as machine learning programs may be, they are even more effective when the human element remains intact. Take, for example, a study highlighted in a recent White House report on preparing for the future of AI. The study compared human and machine in relation to diagnosing cancerous lymph node and found that the AI “doctor” had an error rate of 7.5 percent, while the human had an error rate of 3.5 percent. But together, their error rate fell to just 0.5 percent. And it’s the same story for fraud prevention.
As RISK IDENT explained in a February 2017 PCN article, ahuman being with years of experience fighting fraud can never be replaced by a machine, but a combination of the two entities can produce fantastically accurate results. Domain experts know their fraud problems best, but they need scalable software to help. By constantly feeding their knowledge on the context and causes of fraud into the machine, the system can evolve continually. Fraud managers can therefore scale their fraud protection system by teaching the machines to help monitor for illegal activity.
Designing and implementing AI is also a process that requires a lot of manual intervention from humans. Real people are the ones who decide which models to use, which parameters are correct, and what data is worth feeding into the system. So yes, machine learning fraud prevention methods have the potential to be extremely intelligent. However, they ultimately rely on human decisions from the designing data scientists and engineers, as well as experienced fraud prevention teams, to make the technology efficient, smart, and life-improving.
For more information on how RISK IDENT’s experience with machine learning can help your fraud prevention team, please feel free to contact us at: email@example.com.