Companies are using AI to prevent and detect everything from routine employee theft to insider trading. Many banks and large corporations employ artificial intelligence to detect and prevent fraud and money laundering. Social media companies use machine learning to block illicit content such as child pornography. Businesses are constantly experimenting with new ways to use artificial intelligence for better risk management and faster, more responsive fraud detection — and even to predict and prevent crimes.
While today’s basic technology is not necessarily revolutionary, the algorithms it uses and the results they can produce are. For instance, banks have been using transaction monitoring systems for decades based on pre-defined binary rules that require the output to be manually checked. The success rate is generally low: On average, only 2% of the transactions flagged by the systems ultimately reflect a true crime or malicious intent. By contrast, today’s machine-learning solutions use predictive rules that automatically recognize anomalies in data sets. These advanced algorithms can significantly reduce the number of false alerts by filtering out cases that were flagged incorrectly, while uncovering others missed using conventional rules.
Given the wealth of data available today, and the rising expectations of customers and public authorities when it comes to protecting and managing that data, many companies have decided that this is one of the only ways to keep up with increasingly sophisticated criminals. Today, for example, social media companies are expected to uncover and remove terrorist recruitment videos and messages almost instantly. In time, AI-powered crime-fighting tools could become a requirement for large businesses, in part because there will be no other way to rapidly detect and interpret patterns across billions of pieces of data.
But determining whether AI crime-fighting solutions are a good strategic fit for a company depends on whether the benefits outweigh the risks that accompany them. One such risk is that biased conclusions can be drawn from AI based on factors like ethnicity, gender, and age. Companies can also experience backlash from customers who worry that their data will be misused or exploited by even more data-intensive surveillance of their records, transactions, and communications — especially if those insights are shared with the government. Recently, for example, a European bank was forced to backtrack on its plan to ask customers for permission to monitor their social media accounts as part of its mortgage application process, after a public outcry over its “Big Brother” tactics.