The sheer amount of data available and the need for a reliable real-time system make machines more suited to fraud prevention in a digital setting. But that doesn’t mean you should rely on AI exclusively to detect and prevent fraud. Technology can’t replace the intuition and experience of a team of fraud prevention specialists.

The best approach to fraud prevention consists of recognizing which tasks are better left to AI and which aspects of fraud prevention humans should focus on. You can build a system based on what humans and AI excel at to achieve optimal results.

What can AI do better than humans?

The growth of unstructured data is a trend that makes AI particularly relevant for fraud prevention. It’s difficult to make unstructured data fit into a spreadsheet, and recognizing patterns that connect these unstructured data points can be challenging for a human.

The task of sifting through datasets like your daily transaction log is better suited to AI. This work is time-consuming, and fraud-prevention specialists are more likely to miss something important due to its repetitiveness.

Uncovering patterns and connections between datapoints can also be challenging when analyzing a large dataset. AI can identify emerging trends by comparing recent datapoints with historical data, and adjust risk scores accordingly. It would be difficult for humans to analyze data on this scale.

AI also delivers around-the-clock monitoring. This type of fraud prevention system can operate 24/7 without taking any breaks. It’s a scalable system that can adapt to fluctuation in order volumes during your busy season, and AI can deliver risk scores in real time to avoid creating delays that would affect customer experience.

Fraud prevention specialists can go beyond datapoints

Oversight and decision-making are two areas where humans excel. While AI can make decisions in the context of a rules-based system, fraud prevention specialists can do it even in unfamiliar situations.

Professional experience is an invaluable asset. AI won’t detect a potential scam until sufficient datapoints are available. Humans can sometimes anticipate fraud if a user creates an account with some suspicious characteristics. They can rely on their education or gut feeling, or draw parallels between previous situations they’ve encountered. AI might miss these subtler signals and overlook a fraud if it differs from previous patterns.

In a situation without any historical precedents, AI won’t be able to draw on existing datasets to flag the transaction. A human would recognize red flags immediately, but a fraud prevention system that relies solely on AI and machine learning could be limited by the scope of its training and historical datasets.

While AI is only as good as the datasets used to train it, humans are much better at learning theoretical concepts and applying them in a wide range of unfamiliar situations.

The benefits of using humans and AI together

Mastercard is one of the many companies that use a hybrid approach. Human employees are in charge of controls, system design, manual reviews and creation of new rules based on emerging types of fraud. AI is used to review data and make fraud prevention more accurate and efficient.

Fraud prevention managers can benefit from integrating AI into existing systems:

  • AI is great for automating certain tasks. Go through a large backlog of orders and get more value from existing datasets by identifying meaningful patterns.
  • AI can issue a risk score for each transaction. Fraud prevention specialists can then manually review transactions with a high risk score to eliminate false positives.
  • False positives are one of the main drawbacks of using AI for fraud prevention. They’re one of the reasons it’s best to rely on both AI and human eyes.
  • Manual reviews confirm or infirm the risk score issued by the AI. The manual review process creates more data to further train the machine learning system’s algorithm and achieve more accurate results.
  • Humans can use their expertise to identify the most relevant datasets to use and develop new rules as trends evolve.
  • Using AI allows fraud prevention specialists to focus on high-risk cases. It’s a better use of their time and results in a more effective system.
  • The combination of AI and human eyes results in an optimal experience for customers. AI creates a fast and streamlined process that doesn’t delay order fulfillment, and manual reviews ensure that false positives don’t result in a bad customer experience.

How Fraud.net can help you create a hybrid fraud prevention system

Fraud.net offers an AI-based fraud prevention solution that relies on machine learning to analyze datasets and deliver risk scores for transactions. It’s the perfect candidate for a hybrid system where AI performs repetitive tasks and reviews large datasets while humans focus on oversight, decision-making and manual reviews. 

Request a demo to better understand how this solution can fit into your existing fraud prevention process.