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Top 9 Ways Artificial Intelligence Prevents Fraud


Rule-based engines and simple predictive models could identify the majority of fraud attempts in the past, yet they aren't keeping up with the scale and severity of fraud attempts today. Fraud attempts and breaches are more nuanced, with organized crime and state-sponsored groups using machine learning algorithms to find new ways to defraud digital businesses. Fraud-based attacks have a completely different pattern, sequence, and structure, which make them undetectable using rules-based logic and predictive models alone. What's needed to thwart fraud and stop the exfiltration of valuable transaction data are AI and machine learning platforms capable of combining supervised and unsupervised machine learning that can deliver a weighted score for any digital business' activity in less than a second. AI is a perfect match for the rapid escalation of nuanced, highly sophisticated fraud attempts.

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.

Applying AI to Cyber Risk


As cybercrime and fraud grow increasingly sophisticated, applying AI to cyber risk and to combat fraud as well as to take remedial action, is an important area of innovation. Fraudulent transactions and theft happen daily and many businesses and individuals lose revenue without knowing. Organized criminals and online scammers are increasing the sophistication and scale of their attacks. These attacks are becoming more subtle, with many scamming groups using machine learning algorithms to find new ways to target individuals and online businesses. Traditional approaches are proving inadequate to detect these attacks due to their ever-changing pattern, sequence, and structure. Traditional approaches also don't capitalize on today's technological capabilities.

How machine learning is taking on online retail fraud ZDNet


Amazon Prime Day (APD) was a huge success, they say. If you want to talk numbers though, let's consider this. What would you say if you were told that Amazon could lose nearly 5 percent of that revenue, or $100 million, due to fraud? And it's not just Amazon on its Prime Day, it's every online retailer that is exposed to online fraud every single day. Retail hallmarks like APD or Christmas make things worse.

The Growing Role of Machine Learning in Fraud Detection


Machine learning (ML) can quickly detect fraud, saving organizations and consumers time and money when implemented correctly. As organizations grapple with how to keep up with consumers during the Covid-19 pandemic, they are also dealing with an evolving digital landscape, with online payment fraud losses alone set to exceed $206 billion between 2021 and 2025. While machine learning can save organizations exponential amounts of time and money when implemented correctly, it can also come with some initial challenges. The key to any accurate machine learning model is the input data. Not only does enough historical data need to exist for the model to derive an accurate representation but the data also needs to be accessible.