Credit Unions: Fight Fraud with Machine Learning

Fraud continues to be a costly problem in the financial industry, both for credit unions and the members they serve. As criminals continue to find ways around financial fraud protection measures, credit unions must find ways to combat fraudsters’ innovative methods. 

Fast-evolving fraud attacks put credit unions at a distinct disadvantage because credit unions that rely on rules-based fraud detection models are unable to keep up with the latest fraudulent schemes. Rules cannot meet the scale and speed required by digital transformation.

Machine learning (ML) is the hallmark of a new era of credit union fraud prevention, offering organizations a way to scale their fraud-fighting efforts and protect themselves from known threats — and those that have yet to be discovered. 

Machine Learning’s Role in Fraud Detection

By definition, machine learning is an advanced form of artificial intelligence that can form its own analyses based on the data it ingests. 

Machine learning uses models that are trained based on predefined parameters to detect fraud that fits specific criteria (known as supervised machine learning). Some machine learning models do not rely on predefined parameters and can instead detect unknown types of fraud that trained models may miss (known as unsupervised machine learning). Combined, the two types of machine learning can provide comprehensive financial fraud detection that accounts for known and unknown suspicious activities.

Applied to credit union fraud prevention, machine learning offers a viable way to reduce fraud loss and detect fast-evolving attacks at scale. It closely monitors new account openings and transactions to simultaneously boost conversions and minimize friction for good customers. As a result, credit union fraud teams can take faster action based on the context of activities.

The Increasing Need for Machine Learning in Credit Union Fraud Prevention

The COVID-19 pandemic has underscored the need for better financial fraud protection. The FBI recently reported that cybercrimes are up 300% over the past year. As criminals continue to find ways to circumvent traditional fraud detection systems, credit unions owe it to their members and their reputations to find better, more effective ways to stop fraud.

Using machine learning in credit union fraud detection allows organizations to access and leverage a much larger amount of data at a pace far beyond what humans are capable of handling.

Machine learning for fraud detection has been shown to reduce both fraud and false-positive rates at financial institutions. For example, one global payments network increased its fraud detection by 20% with 94% accuracy while accelerating fraud model development by 5x.

DataVisor’s Multi-Layered Machine Learning Approach Creates Unique Advantages

Credit union fraud prevention is a top priority for DataVisor, which is why we take a multi-layered, proactive approach to detection. 

DataVisor’s open platform supports seamless consolidation and enrichment of any data, making it infinitely scalable. That means that credit unions can act on fast-evolving fraud and money laundering activities in real time.

DataVisor helps credit unions:

• Stay ahead of savvy fraudsters with proactive detection
• Increase conversions by reducing customer friction
• Make contextual decisions quickly
• Maintain full control of their data, models, and decisions

Want to see DataVisor’s multi-layered approach to fraud detection with machine learning? Request a demo to see how you can proactively defeat new and fast-evolving fraud attacks with a comprehensive AI-based solution.