Reduce False Positives In Fraud Management With Artificial Intelligence

Businesses may incur greater losses from false positives than from fraud itself.

The involvement of AI in fraud detection can reduce the number of false positives from the process.

Organizations generally tend to spend thousands of dollars on preventing financial fraud from their digital transactions. While such measures may be useful for the purpose, there is another issue that needs sorting out: the number of false positives present amidst the fraudulent transactions detected. False positives are the legitimate transactions incorrectly flagged as fraudulent by your fraud detection tools. A surprisingly high number of credit card transactions are incorrectly labeled as illegitimate by fraud detection systems in businesses. As you know, every transaction flagged as fraudulent needs to be investigated thoroughly by your financial department. Such investigations are expensive, meaning that false positives may end up being more loss-making for businesses as compared to actual fraudulent transactions. Therefore, in 2020, 66% of businesses cited the prevention of false positives from fraudulent transactions as their top concern.

There are already ways in which machine learning and AI impacts financial industries and operations. In the same vein, involving AI in fraud detection can prevent legitimate, legal transactions from getting flagged.

How Machine Learning Reduces False Positives

Machine learning can be used to create fraud alerting systems. Such systems are configured to scan and identify behavioral patterns of payers from past transactions—known as "features." The algorithms in such systems can accurately detect abnormal transactions and features in a given transaction. Context-based filters in such systems enable you to receive alerts for only specific types of features. As we know, machine learning algorithms perform more and more accurately with time as the scope of their "knowledge" keeps increasing with each transaction.

As a result, machine learning adopts behavioral pattern recognition to bring greater accuracy into your fraud detection processes.

How Banks Can Implement AI to Eliminate False Positives

Danske Bank, a Danish multinational financial institution, faced an issue of its manual rule-driven fraud detection system incorrectly flagging up to 99.5% of all transactions—up to 1200 transactions on a daily basis—it conducted as fraudulent. At the same time, the system could only correctly detect up to 40% of actual fraudulent transactions. To resolve this, the bank adopted a machine learning-based system that reduced its false-positive cases by 60% and increased the accuracy of fraud detection to as high as 80%.

False negatives allow fraudulent transactions to go unaddressed, while false positives flag legitimate transactions as fraudulent, causing inconvenience to payers. Ideally, your organization must prevent both. Involving AI in fraud detection enables your organization to achieve that target.