Fraud has long been a major issue for financial services institutions. And as global transactions have increased, the danger has too. Fortunately, artificial intelligence has enormous potential to reduce financial fraud. As automated fraud detection tools get smarter and machine learning becomes more powerful, the outlook should improve exponentially. In its latest report, security company McAfee estimates that cybercrime currently costs the global economy some $600 billion, or 0.8% of global gross domestic product.
You're sitting at home minding your own business when you get a call from your credit card's fraud detection unit asking if you've just made a purchase at a department store in your city. It wasn't you who bought expensive electronics using your credit card – in fact, it's been in your pocket all afternoon. So how did the bank know to flag this single purchase as most likely fraudulent? Credit card companies have a vested interest in identifying financial transactions that are illegitimate and criminal in nature. According to the Federal Reserve Payments Study, Americans used credit cards to pay for 26.2 billion purchases in 2012.
As we make more cashless payments for retail purchases, restaurants, and transportation – not to mention the increase in online shopping – wallets loaded with legal tender may become a thing of the past. According to 2018 research by BigCommerce, software vendor and Square payment processing solution provider, 51 percent of Americans think that online shopping is the best option. Last year, 1.66 billion people worldwide bought goods online. And the number of digital buyers is expected to exceed 2.14 billion. Unfortunately, growing sales may mean not only greater revenue but also bigger losses due to fraud.
Machine learning is a field of science that offers machines an ability to understand data and carry out processes just as a human would do. The ML technology uses complex algorithms to analyze large data sets and find data patterns that help in business decisions. This is why machine learning can detect fraud in the system easily. It is, in fact, used for various other purposes such as spam detection, product recommendation, image recognition, predictive analysis, etc. Gartner predicted that by the year 2022, the machines would be analyzing 50% of the data, which is only 10% more from the present scenario. Since machines are far better at detecting patterns, ML can analyze huge sets of data in one chance and find fraud-related behavior through cognitive technology.
During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me. One important detail is missing in this scenario – continuous flow of transactional data is not needed for model retraining.