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How Machine Learning Can Improve Fraud Detection in Real Time - DZone AI


"Machine learning" is a computer science discipline that refers to the ability for machines to learn with data and carry out tasks that would typically require human intelligence. The technology is growing quickly: according to Gartner, more than half of data and analytics services will be performed by machines rather than human beings by 2022, which is 10 percent more than today. The emergence of machine learning and its implementation into consumer facing applications coincides conveniently with today's real-time economy. Machine learning drives a decrease in fraud before it impacts the victim, just as our society has become as impatient as ever. In fact, more than 60 percent of people increasingly feel that waiting for something that should happen instantaneously impacts their perception of the underlying brand -- which is especially true when it comes to identity or financial fraud.

Why Machine Learning Is The Best Way To Reduce 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.

Reasons Why Machine Learning Is The Best Way To Mitigate Fraud TechBullion


In 1997 when Deep Blue, a supercomputer, beat the then chess champion, Garry Kasparov, we all were taken aback. That was more than 20 years ago and most of us did not even know that computers like that even existed. In late 2017, AlphaZero taught itself how to play chess under just four hours and beat the world's then best chess-playing computer program. Remember, AlphaZero, the game-playing AI created by DeepMind, was not taught any domain knowledge but the rules of the game. Such is the power of machines to learn and improvise and industries across the world are tapping a machine's ability to learn and improve from its experience without being explicitly programmed.

Translytical Databases Hit the Ground Running


The push for operational analytics that seeks to eliminate the requirement for constantly moving data between storage and databases to support transaction and analytical workloads is fueling the growth of translytical data platforms. The framework uses a single data tier that can serve both transactional and analytical workloads. The requirement to reduce data movement between data silos and technology stacks along with rise of machine learning and streaming analytics has fueled the rise of a maturing translytical data platform sector. Market watcher Forrester released a market survey earlier this month that identifies and ranks a dozen key players in the nascent analytics sector. The market researcher also identified the top translytical database workloads, including: real-time applications; Internet of Things (IoT) analytics operational data; connected data apps; and continuous learning in which translytical databases are used to train and retrain machine learning models.

The In-depth 2020 Guide to E-commerce Fraud Detection


It is hard to underestimate the role of E-commerce in a world where most communications happen on the web and our virtual environment is full of advertisements with attractive products and services to buy. Meanwhile, it is obvious that many criminals are trying to take advantage of it, using scams and malware to compromise users' data. The level of E-commerce fraud is high, according to the statistics. With E-commerce sales estimated to reach $630 billion (or more) in 2020, an estimated $16 billion will be lost because of fraud. Amazon accounts for almost a third of all E-commerce deals in the United States; Amazon's sales numbers increase by about 15% to 20% each year. From 2018 to 2019, E-commerce spending increased by 57% -- the third time in U.S. history that the money spent shopping online exceeded the amount of money spent in brick-and-mortar stores. The Crowe UK and Centre for Counter Fraud Studies (CCFS) created Europe's most complete database of information on fraud, with data from more than 1,300 enterprises from almost every economic field.