Digital fraud is as old as the internet itself, and bad actors continuously develop new techniques while refining old ones. Fraudsters can deploy old-fashioned confidence schemes on a far greater scale than they can in person, with fraudsters leveraging social engineering and phishing schemes to convince victims to give up information of their own accord. Other bad actors wield high-tech methods such as botnets, brute force attacks and credential stuffing, automating these tactics via artificial intelligence (AI) and machine learning (ML) to conduct thousands of attacks every hour. Nearly half of all organizations reported being targeted by fraud this year, stemming from a wide variety of sources. Almost 70% reported attacks from external sources, with some of the most common being hackers and organized crime rings.

Several day-to-day chores have gotten significantly simpler because of the extraordinary growth in the usage of smart devices, user-friendly mobile applications, and progressive data/information management solutions. For example, if you ever want to send money to anyone thousands of kilometers away, you may do it in a matter of minutes. However, as things have become easier to accomplish in the digital world, there has been a massive increase in incidents of digital fraud. As a result of the numerous examples of data theft and digital fraud discovered by cyber security, it is now more necessary than ever to implement countermeasures to prevent such incidents while improving fraud detection efficiency. AI and machine learning, according to technology experts, may be used to detect fraud and reduce scams in the healthcare, eCommerce, and finance industries.

Digital fraud continues to flourish, with recent surveys finding that security breaches have increased 67 percent since 2014 and 11 percent since 2018. Casualties of these breaches in the first half of 2019 alone include 4.1 billion personal records exposed in a variety of ways: 52 percent through hacking; 33 percent via phishing; and 32 percent through social engineering, with many involving more than one method. Organizations and security developers are investing billions of dollars in fighting these fraud attempts. Worldwide spending on security systems is projected to hit $131 billion by the end of 2020, and $174 billion over the next two years. Artificial intelligence (AI) and machine learning (ML) applications often form the core of these cybersecurity systems and are being deployed across banks, retailers, telecommunications companies and many other businesses.

Throughout history, merchants have had to contend with fraud. So long as there's money to be made, criminals will try to exploit any vulnerabilities, and so long as there's money on the line, merchants will fight back. In response to fraudulent transactions in the physical world, merchants turned to EMV chip card authentication at the point of sale. This was widely successful, and levels of fraudulent card-present transactions have plummeted in recent years. However, criminals responded by turning towards digital channels to carry out new fraud vectors.