Fraud analytics is a method of parsing large sets of transaction data to spot suspicious transactions, patterns of fraud or anomalous behavior. Typically, fraud teams leverage technologies such as machine learning to do this work.
Fraud analytics plays an important role in fraud detection. The information gleaned from detection can then be used to mobilize fraud prevention resources, both analysts and fraud prevention software, ideally to intervene before a victim’s money leaves their account. Fraud analytics is also used to investigate potential instances of fraud after the fact.
Below, Featurespace Subject Matter Expert Steve Goddard, explores why fraud analytics is so important, and how financial institutions use it to preempt scams and theft.
Online fraud is on the rise
The pandemic drove digital transformations across many sectors of the economy, and those transformations created numerous opportunities for fraudsters. In finance and commerce, especially, mass movements of consumers to online shopping and online banking created millions of new fraud targets in a short amount of time.
By the spring of 2021, online fraud in the UK had risen by a third, and in the United States fraud attempts had risen by one-quarter. In the time since, those numbers have only grown. The rise in new digital banking customers facilitated scams like account takeovers, for example, while the rise of new online retailers facilitated scams like merchant bust outs and triangulation fraud.
But digital criminals tend to leave behind digital clues. This is where fraud analysis comes in.
By mining past and current transactions that get flagged as suspicious, financial institutions and their customers can take proactive steps toward mitigating fraud and, ultimately, preventing people from losing their hard-earned money.
Real-time fraud analysis to detect fraudulent behavior
Featurespace’s ARIC™ Risk Hub is a good example of how machine learning can be used in fraud analytics to prevent instances of fraud.
Our Adaptive Behavioral Analytics technology monitors customer data in real time to understand how people transact. This allows the platform to learn what is normal behavior and what is abnormal behavior.
When it detects abnormal behavior, ARIC Risk Hub can assess the likelihood that the behavior represents an act of fraud instantly. That way, the fraud analysts at that person’s bank can intervene and even halt the transaction if they think a customer is being defrauded.
These are the kinds of tools that major banks, payment processors and merchant acquirers rely on to stop fraud in its tracks.
Fraud analytics in banking
Here is an example of how banks put fraud analytics to work.
Contis, Europe’s No. 1 provider of end-to-end banking, payments and processing services, had relied on an in-house fraud management solution for years. That system was a rules-based system, which made Contis’ fraud detection and prevention capabilities a little rigid. Rules have to be periodically examined and re-tuned in such a system.
That solution also failed to give Contis a holistic view of customer transactions. As a result, its fraud alert system produced too many false positives and risked creating unnecessary friction for legitimate customer transactions.
Against the context of fraud’s proliferation, the fraud team at Contis recognized that it was time for a new fraud management system.
And so, they implemented the ARIC Risk Hub over the course of 90 days, which allowed Contis to integrate customer data and transaction data, as well as external third-party data. With that data, Contis was able to set up predictive analytics and set custom rules to pinpoint specific fraud types.
With those new capabilities, Contis suddenly had one of the best rates of fraud prevention in the industry, and overall fraud dropped by more than 80 percent.
To see how ARIC Risk Hub’s real-time fraud analytics capabilities work, have a look at this video.
Fraud detection analytics in other sectors
Fraud analytics has applications outside of banking, too:
- In the gaming and gambling sector, organizations need the ability to spot suspicious deposit and withdrawal activity quickly.
- Acquirers and card issuers also need similar capabilities.
- In retail, fraud analytics can be helpful in spotting scams such as chargeback fraud.
Protect your business and customers with our fraud monitoring analytics software
Fraud monitoring analytics software is important for mitigating fraud losses, maintaining regulatory compliance and ensuring your company delivers the best customer experience possible.