Machine Learning: 6 Ways it’s Helping Lead the Fight Against Fraud

Whether you’re browsing Amazon to place an order, visiting a medical specialist to discuss the possibility of surgery, or catching the subway to work, artificial intelligence and machine learning are at play all around us. And they’re only growing more prevalent. 

Several industries, such as banking, healthcare, retail and more have already adopted some form of artificial intelligence into parts of their business operations. And others are well on their way to following suit. 

Moreover, approximately 82 percent of top machine learning use cases are related to risk management, so it should come as no surprise that when it comes to mitigating fraud risks, machine learning is already taking center stage–and making an impact. Here are the top six ways it’s it’s already transforming the fraud landscape: 

1. Automated Detection: Machine learning algorithms can quickly and accurately analyze large amounts of data over hands-on processes that require human intervention. For context, a Forbes article states that AI and Big Data technologies are capable of automating almost 80 percent of physical work.  This level of automation makes it easier than ever for machines to identify patterns and anomalies that may indicate fraud over human processes. Automated detection can also happen in real-time, reducing the time and cost associated with manual fraud detection approaches. Human intelligence efforts can then be reserved to supplement machine learning and AI efforts instead of serving as the default mechanism for fraud detection. 
2. Improved Accuracy: A huge plus with machine learning models is that they can learn and adapt to new fraud patterns, allowing them to make more accurate predictions about suspicious behavior. This can significantly reduce the number of false positives and false negatives, leading to a more effective fraud prevention system.
3. Predictive Analytics: Another great benefit of machine learning is that it can be used to develop predictive models that can help prevent fraud before it occurs. By analyzing historical data, machine learning models can identify patterns and trends that may indicate future fraudulent activity. This can include identifying high-risk transactions or users and flagging them for additional scrutiny. 
4. Behavior Monitoring: According to Javelin, identity fraud scams theft comprised $43 billion in losses in 2020. Although fraudsters are gaining access to consumer identities more successfully, machine learning algorithms can easily detect deviations in purchasing behaviors in real-time, identifying suspicious activity before it results in fraud. This can include analyzing login patterns, device usage, and transaction history to identify potential threats.
5. Fraud Network Analysis: Fraud is rampant—and it’s often digital. Machine learning can be used to root out fraud networks by identifying relationships between individuals and organizations involved in fraud. This can be helpful to investigators as they identify the causes of fraud and take action to prevent future incidents.
6. Real-Time Decision Making: Machine learning algorithms can make decisions in real-time, allowing fraud prevention systems to respond to suspicious activity quickly. This can include blocking transactions or freezing accounts to prevent further fraudulent activity—something we often see credit card companies do. American Express, for instance, uses AI to monitor transactions and has caught and prevented at least $8 billion in credit and fraud risks as of the publication of the Forbes article where this data is featured. 

Machine learning and artificial intelligence aren’t just a fad. They’re technologies that are here to stay and will ultimately leave their impact around the globe. According to McKinsey research, artificial intelligence is set to add around 16 percent to global output by the year 2030, which equates to about $13 trillion in GDP. 

While many industries are already relying on these technologies or planning to in the very near future, Point Predictive is already at the forefront of leveraging them in innovative ways to combat fraud for our clients. 

Point Predictive’s stance on machine learning

At Point Predictive, we use many of the aforementioned machine learning approaches to help our clients be on the preventive end of fraud mitigation. Our solutions save our customers millions each month and prove that as fraudsters become more sophisticated, machine learning will continue to play an important role in keeping businesses and consumers safe from fraudulent activity. 

Point Predictive complements our Ai based solutions with what we call “natural intelligence” or Ni. Applications that receive the highest risk scores are prioritized for review by our analysts, supplementing the automated risk scoring with human intelligence.

To learn more about Point Predictive and its cutting-edge solutions, speak with our solution experts.