Producing a fount of business benefits One study last year pegged the annual savings reaped by credit card issuers deploying machine learning fraud detection systems at $12 billion annually. Machine learningcan foster better credit customer relations. All too often credit cards are ‘flagged’ for what static models perceive as abnormal behavior, such as a purchase in a foreign country. Machine learning instead can perceive this as ‘normal’ behavior if the same card was used to book an airline flight, a hotel room in the same foreign land, and perhaps pay for a meal at an airport. Also, marketers have given machine learning a warm embrace. They use it to crunch through enormous volumes of customer data, augmented by third party data on consumer patterns and behaviors, to develop far more targeted and therefore cost-effective online marketing campaigns as well as in-store promotions. Rent The Runway, a start up seeking to disrupt the venerable business of selling high-end designer dresses, leverages an as-a-service machine learning solution to accurately target customers seeking to rent rather than buy this costly apparel. The bottom line is that machine learning programs are capable of ingesting and analyzing tidal volumes of data, andany combination of variables you want to stick into the program (income, zip code, banking history, etc.). Given that data volumes by most estimates are doubling every two years in most businesses, machine learning has arrived at a perfect time given its ability to handle these massive data volumes. Moreover, machine learning does this work tirelessly on commodity – meaning inexpensive – hardware.
If you are new to machine learning or feel daunted by its seeming complexity, think of it this way. Computers, which are inherently dumb, get their smarts from programmers that feed them instructions, which the computer follows explicitly. With machine learning, a computer is equipped with highly complex algorithms - which are similar to programs - along with mountains of data. Only the computer then acts and learns to ‘think’ on its own as it discerns patterns and anomalies in that data. In fact, the more data it is fed over time, the more the machine ‘learns’. It is the increasing need for automation in credit decisioning helping to drive the creation of sophisticated machine language algorithms. These machines can even have fun. Last year a machine learning program called AlphaGo from Google DeepMind beat the world’s top player of Go, a highly complex and ancient Chinese board game. AlphaGo was not programmed to play Go, but rather learned to play on its own as the games progressed. Several vertical markets, including financial services, are already heavily using machine learning techniques. In financial services, machine learning has taken firm root in fraud detection, showing capabilities to not only detect fraud that has happened but, increasingly, to actually predict when fraud is likely to occur so actions can be taken to prevent it. Machine learning does so by recognizing patterns of transactions or behaviors that indicate fraud – patterns that might well have revealed themselves to human detection methods. Machine learning is partic- ularly adept and finding these previously hidden patterns.
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