Table of Contents
- Market Categories and Deployment Types
- Key Criteria Comparison
- GigaOm Radar
- Vendor Insights
- Analyst’s Take
- About Michael Azoff
The use of artificial intelligence (AI)—typically in some form of machine learning (ML) designed for anomaly detection—is the norm today across the leading solutions in fraud monitoring and detection, whereas the previous generation technology exploited business rules and data mining and analytics. And while both data rules and data mining continue to play a role in new generation products (business rules are useful to implement policies and data science is essential for ML model feature engineering), the widespread adoption of AI has led to improved accuracy in detecting fraud with fewer false positives, giving fraud investigators greater confidence in the technology.
With the use of AI/ML now commonplace, differentiation in this space instead becomes a question of how the ML is used. For example:
- Is the ML model supervised or unsupervised?
- Does the vendor create a custom ML model specific to the customer or supply a ready-built model, which is then fine tuned and retrained for each customer over time?
Customer data privacy is strictly respected, and with AI/ML vendors are able to see trends appearing in their customer base and can alert customers not yet affected by a new fraud. Vendors with large customer bases and those who subscribe to fraud data alert services have an advantage here.
Some of the newer types of fraud relate to the digital economy, such as exploiting voucher schemes and return policies (which are more generous than ever before as retailers aim to compete with Amazon). Unlike a transaction fraud—which is either allowed or stopped—the newer types of fraud require a more nuanced approach because a good customer might have picked up a bad behavior and it is the behavior that needs to be stopped while retaining the customer. For example, it’s a gray area merging into abuse when a customer buys 10 items online (which are free to return) to only keep one.
One common approach that many vendors take is to model normal behavior and then train the ML model to detect any deviation from the norm—being open ended in this manner allows new forms of fraud to be detected earlier. Combined with various risk assessments, the abnormal incident may then be flagged for escalation. For example, more stringent ID checking may be performed before bringing the incident to the attention of human fraud investigators.
AI/ML has brought significant benefits to the automation of fraud detection, greatly improving banks’ and merchants’ abilities to deal with the rise in fraud in the wake of economic digitization. For more background information on this topic, we recommend readers refer to the GigaOm report “Key Criteria on Intelligent Fraud Detection Solutions.” In this Radar report, we perform an in-depth side-by-side evaluation of the leading intelligent fraud detection (IFD) products in the market.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding, consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.