Collaboration between Human and Artificial Intelligence (AI), the Future of Fraud Prevention
Fraud has always been a challenge for financial institutions, especially with the development of the digital world. Digital innovation opens more opportunities for fraudulent acts and brings threats to a higher level that made financial institutions become more vulnerable to such threats.
However, like two sides of a coin, as there are threats, there are opportunities as well. Financial institutions can manage thousands of data to overcome and even prevent fraud from occurring by adopting artificial intelligence (AI) – a device capable of mimicking human cognitive functions related to ‘learning’ and ‘problem solving’.
Through deep-learning machine, complex big data can be useful to prevent fraud. The existence of AI makes humans no longer need to perform routine activities because it has been automated. Most importantly, AI is able to analyze the data in real time and recognize the signs of fraud that human analysts may miss, thereby increasing the chances of fraud prevention.
The presence of AI so far is not to replace human tasks, but to complement the human capacity itself as it was done by PayPal. They hired detectives to train the machine so they can recognize patterns of fraudsters. They develop good and bad user behavior scenarios so that machines can learn the signs of a fraud.
If the usual analytical software is able to consume 20-30 variables, then the deep learning technology offered by AI is able to analyze information and more sophisticated patterns. For example, regular analytics software will mark as suspicious an account which is accessed by five different internet protocol addresses within five days. However, AI’s deep learning ability will analyze the situation more deeply, for instance, the account could be owned by a pilot who was shopping souvenirs for his family.
In practice, AI is not alone in performing its functions. PayPal uses homegrown artificial intelligence machines built with open-source tools, which needed the human analyst’s contribution in its operations. Such efforts minimize the chance of false alarms while minimizing the false alarm rate itself becomes the team’s primary goal. The reduced level of false alarms makes the team more focused on overcoming the actual fraud. In addition, avoiding false alarms are associated with the convenience of the user, which is avoiding the occurrence of legitimate client account blocking.