Fraud Detection Machine Learning

Solve complex fraud detection challenges using machine learning.

modern architecture
Radial circle

Top considerations for fraud detection machine learning:


1. Machine learning used for fraud detection helps data scientists determine which transactions are mostly likely to be fraudulent and result in significantly reduced false positives. 

2. If properly created and deployed, machine learning can distinguish between fraudulent and legitimate behaviors.

3. Proper understanding of the fraud domain, along with fraud-specific data science techniques, are required to ensure that machine learning platforms don’t learn the wrong thing.

4. Both supervised and unsupervised models are important components of a comprehensive fraud detection strategy. 

Models for fraud detection machine learning:


Machine learning provides the foundation for highly effective fraud detection. These models are extremely effective in fraud prevention and detection. They enable automated discovery of patterns across large volumes of streaming transactions, where they distinguish between legitimate and fraudulent behaviors, while adapting to new and previously unseen fraud strategies. 

Methods and applications for fraud detection machine learning:


Machine learning, when leveraged for fraud detection, is one of the most effective ways to interpret an individual’s financial behaviors while also supporting an ever-evolving array of secure and convenient payment transactions. To be effective, the models must be tailored to a specific use case. A cohesive fraud detection strategy requires both supervised and unsupervised machine learning models. 

Supervised machine learning for fraud detection

This is the most common form of machine learning, where models are trained using a set of properly tagged transactions. The models ingest massive amounts of tagged transactions, noted as fraud or not fraud, to learn patterns that reflect legitimate behavior. This enables the models to learn exactly what patterns it should look for. 

Unsupervised machine learning for fraud detection

Unsupervised learning is harnessed to detect outliers that represent previously unforeseen forms of fraud. This technique detects anomalies that don’t conform to the majority. For accurateness, these discrepancies are then evaluated individually as well as through sophisticated peer group comparison. 

Using an optimal blend of supervised and unsupervised machine learning techniques enables you to detect new forms of suspicious behavior while also allowing you to recognize more subtle patterns of fraud.

FICO fraud detection machine learning solutions 

Transform your fraud detection and prevention with FICO’s proven machine learning innovations. Join more than 9,000 financial institutions around the globe that trust FICO to identify individual behaviors that require attention while remaining invisible to consumers during legitimate transactions. To learn more about how machine learning and artificial intelligence can help prevent enterprise fraud, contact us to speak with an advisor. 

Contact Us

Connect with FICO for answers to all your product and solution questions. We look forward to hearing from you.