While the term may be relatively obscure, we benefit from machine learning everyday. Watch a few British crime dramas and suddenly your Netflix queue is populated with a stream of related movies and TV shows. These highly curated suggestions are based on the viewing patterns of thousands of similar customers. With each new user or selection, these high-powered computers become smarter — learning over time until they know us almost as well as we know ourselves.
What is Machine Learning?
At the most basic level, machine learning is another name for teaching computers to learn for themselves. A way to predict, prevent and respond to fraud, machine learning is a powerful blend of applied math and computer science. By teaching computers how to behave and perform complex tasks, machines are able to predict future outcomes. Using statistical learning, these highly sophisticated computers use rational, if-then statements to identify boundaries, define data patterns and make highly accurate predictions.
While machine learning is an asset for almost any industry, it is becoming an increasingly powerful risk management tool for companies hoping to identify and prevent fraud. As technology evolves, online fraud is becoming more prevalent and more damaging, particularly for retailers who operate across platforms (apps, desktop and mobile). As e-commerce grows, companies are experiencing a proportional rise in fraudulent transactions. In 2014, 65% of organizations with revenues in excess of $1 billion were victims of online payment fraud, and by 2015 every $100 of fraud actually cost merchants $223. With over 90% of companies experiencing online fraud, machine learning gives retailers access to powerful tools that were previously unattainable.
The Evolution of Machine Learning
Throughout their 60-year existence, computers have been a grand experiment in machine learning — making strides in complexity and accuracy with each passing year. Early computers were only able to read digits and text, but today’s computers are trained to recognize context and sentiment in ways that help them predict a user’s behavior. This kind of technology is the driving force behind corporate chatbots and virtual assistants like Siri. In the financial realm, machine learning can examine and analyze thousands of data sets to detect and reject fraudulent transactions — saving time and manpower in the process.
Machine Learning in the Real World
While media and retailers have been using machine learning for years to echo and amplify consumer tastes based on prior purchases and viewing habits, machine learning is also improving the operability of self-driving cars and automated assistants. Though machine learning uses AI to make decisions in the same way a human brain does, these powerful computers employ complex algorithms to analyze millions of individual transactions, making real-time evaluations quicker and with more accuracy than any human. As payments spread across platforms, from credit and debit cards to ATMs, smartphones, desktops and mobile devices, machine learning has become an increasingly useful tool for combating fraud.
Unfortunately, criminals have also become more adept at using big data and analytics to disguise their crimes, making it harder for businesses to approve or reject transactions. Detecting system vulnerabilities, fraudsters employ big data and the dark web to seek out vulnerabilities and maximize their monetary gains. To fight back in this brave new world of fraud, companies must be able to fend off attacks in real time, making accurate cause-and-effect predictions within seconds. Computers then use these insights to adjust their algorithms — learning and processing at speeds far faster than the human mind. Besides its advantages in speed, machine learning can accurately identify fraudulent transactions by constantly processing and analyzing new data sets, thus minimizing the time and expense of costly manual reviews.
How Machine Learning Fights Fraud
Soon after the first credit card was invented more than 50 years ago, digital fraud became a normal cost of doing business. Back in those days, big data consisted of physical books listing thousands of hot credit card numbers that were pored over by fraud detection specialists. While the process has become automated, identifying suspicious activity remains a necessary evil, with businesses determining, in a matter of seconds, whether to approve or decline a transaction. If they incorrectly identify a transaction as fraud, companies run the risk of losing a customer. As computers become more robust and statistical analytics and analysis improves, machine learning is replacing legacy fraud management systems. With a huge amount of data attached to each transaction, the buying habits and patterns of good and bad customers can be identified long before any fraud occurs. According to the Global Fraud Attack Index, at the beginning of 2015, less than $2 out of every $100 was subject to a fraud attack, but by the beginning of 2016 it was $7.30 out of every $100. With fraud attempts almost inevitable these days, the manpower required to prevent fraud can be almost as expensive as the fraud itself. Enter machine learning, which utilizes both historical and live data to create patterns that can predict customer behavior. From identity verification and payment authorization to checkout scoring and merchant underwriting, machine learning is useful for analyzing large amounts of data for decision-making purposes. Replacing time-consuming, rules-based management tools, organizations can use machine learning to leverage the power of big data — performing analytics and delivering risk scores efficiently, in real time and with greater accuracy.
Drawbacks to Machine Learning
As amazing as machine learning can be, even the most advanced machines can’t replace the power of human decision making. While computers can make decisions based on patterns, they do not do well with aberrant data like holiday shopping patterns. Furthermore, machine learning is only as accurate as the data it is given, so inappropriate or erroneous data will result in irrelevant fraud scores. There must be enough relevant data to identify legitimate cause-and-effect relationships. And there is always the risk that a machine may teach itself the wrong thing. Self-learning models are great at identifying fraud, but it is almost impossible for a human to track, control or adjust what the machine learns, which could be bad if it draws incorrect insights and begins blocking good customers.
While computers excel at detecting objective trends and links that humans are unable to spot, machines may be able to learn, but they are still unable to think. They can only process the data they are given, and they are unable to find insightful solutions to problems. Fraudsters are almost impossible to predict, so it’s often difficult — even for machines — to tell the different between a criminal and a genuine customer. This problem is exacerbated by the fact that hackers do their best to make their profiles look more convincing by adopting the characteristics of a good buyer. And while machines can reduce labor costs and improve fraud detection precision, they won’t replace humans completely. Machines excel at “big data” and finding patterns in huge datasets, but fraud is often about small data. Many companies use machine learning up to a point, and then hand problems over to manual reviewers who examine transactions flagged as fraudulent. According to the Annual Fraud Benchmark Report of 2016, 83% of U.S. merchants still rely on manual reviews. Even though 30% of all declined orders marked as fraudulent are likely legitimate, fraud attacks still increase every quarter. Since businesses do best when transactions are user-friendly and hassle-free, machine learning can still help protect company revenue and customer data.
The Best of Both Worlds
Perhaps the best method of fraud prevention and detection is for human intelligence and artificial intelligence to work together. While machines examine transactional data and consumer buying patterns, fraud specialists can use their logic and fraudster know-how to continually improve fraud detection techniques. Fast, smooth and designed to scale, machine learning is capable of making accurate decisions in fractions of a second — helping businesses become more efficient and protecting their revenue, reducing fraud losses, streamlining manual review, increasing sales, maintaining customer loyalty and reducing false positives. While machine learning is helping businesses and financial companies stay one step ahead of fraudsters, Point-to-Point Encryption (P2PE) solutions that secure payment data the moment a card is swiped can add another layer of protection for your business. To keep your company safe and secure in the years to come, contact Bluefin today to learn more about our seamless P2PE solutions.