There’s a lot of buzz these days around Machine Learning in general and how it is being applied across a variety of disciplines to help businesses innovate and deliver new capabilities to market. Whether it’s pharmaceutical companies employing Machine Learning to develop new life saving drugs or hedge funds using it to identify arbitrage opportunities in the stock market, Machine Learning, while still in its infancy, promises to be one of the major technology trends that will transform the way we make sense of the massive amounts of data being generated on a daily basis.
Beyond the hype of what Machine Learning might deliver, one area where this technology is being applied today is in the realm of real-time fraud detection. Fraud is a particularly useful domain for applying Machine Learning since in order for it to be effective, the system requires lots and lots of data to train and fine tune its predictions over time. With millions of transactions being generated on a daily basis in industries like financial services, retail, and telecommunications, the opportunity for forward thinking organizations to apply Machine Learning to their fraud prevention strategy is ripe.
iovation recently hosted a webinar on how Machine Learning can help companies improve their fraud prevention posture. The webinar outlined five core benefits that Machine Learning can be used to help companies reduce their exposure to fraud while improving the customer experience. These benefits include:
5. Better Device Recognition: Our devices change over time, whether via organic changes like upgrades or intentional changes by fraudsters who are looking to evade recognition. For businesses, accuracy is critical when it comes to distinguishing good versus bad devices and Machine Learning can help fraud managers recognize how a device changes over time, helping them train their focus on the most high risk transactions.
4. Uncover Subtle and Complex Risk Patterns: Humans are pretty good at recognizing patterns in small data sets however our brains simply cannot process information at scale. Machine Learning enables you to leverage sophisticated computational and automated methods that can scan through reams of data and quickly identify meaningful risk indicators.
3. Predicting Risk Patterns That You Haven’t Seen: Most businesses rely on traditional rules to identify fraudulent behavior. However, rule based engines don't do a particularly good job of identifying fraud patterns that have not been seen before. For instance, iovation customers benefit from Machine Learning through insights gleaned by our community of 4,000 fraud experts. These insights are then fed into SureScore, iovation's Machine Learning engine that then helps generate business rules to ward off the latest breed of threats.
2. A Superior User Experience for Trusted Customers: Fraud teams must constantly walk a fine line between creating systems and rules that can spot fraud before a loss occurs while ensuring their good customers aren't turned away. A fraud prevention strategy that embraces Machine Learning can help eliminate unnecessary friction, expedite order processing to improve the customer experience, and even dynamically offer risk-free promotions/incentives to your most trusted customers.
1. Create a More Comprehensive Fraud Prevention Strategy: Machine Learning is by no means a silver bullet, however, when it's combined with comprehensive traditional business rules, companies can benefit from a layered fraud prevention strategy that blends the insights of human experts with the unrivaled pattern matching abilities of raw computational power.
To learn more about how Machine Learning can improve your online fraud prevention, watch the 30 minute webinar: