Fraud analytics: new analysis technology for better detection and prevention

Strategic use of data analytics techniques is absolutely key in fighting fraud today, all across industries. Fraud analytics is the use of big data analysis techniques, combined with human interaction, to detect and investigate suspicious activity or improper transactions connected to financial fraud. 

Fraud analytics can be used for prevention, detection, or both. Analytics can help predict future fraudulent behavior, which helps manage risk, detect fraud schemes faster in real time, and scale detection. 

In this article we’ll give a quick overview of what fraud analytics is, what its value is for businesses, how it works, and key benefits. We’ll also take a look at how graph analytics can contribute to faster, more efficient fraud analytics.

Fraud analytics is the use of data analytics techniques to prevent and detect fraudulent activities. 

Some common techniques used in fraud analytics include anomaly detection, predictive modeling, network analysis, and machine learning. By leveraging these technologies, fraud analysts can identify suspicious activities and transactions in real-time, investigate them further, and take appropriate actions to prevent further fraud.

Fraud analytics can help organizations to reduce their financial losses due to fraudulent activities, protect their brand reputation, and comply with regulatory requirements. It can be applied to a wide range of fraud types, such as identity theft, credit card fraud, or insurance fraud.

The world we’re living in isn’t the same as ten years ago, or even five years ago. The ways we do business and bank have changed profoundly. All kinds of transactions happen online and across devices. 

Think about banking specifically. Financial services have moved increasingly online and customers can access their bank accounts from anywhere: a mobile app, an online portal, on the phone, in person at a physical agency, increasing risk. Banks must walk a fine line to balance good customer experience with security.

Without additional controls and detection systems put in place, accounts may also be easy for fraudsters to access. Login credentials are easily available for purchase on the dark web. Financial institutions need additional controls and tools to protect customer accounts when a username and password no longer suffice to prove someone’s identity. 

The environment of increased fraud risk coincides with an increase in regulatory pressure around not only fraud, but also corruption, money laundering, and financial crime in general. 

Fraud prevention and detection is like an arms race. As fraud techniques evolve - and they evolve quickly - financial institutions put new measures in place to meet those new challenges. And then the fraud techniques evolve once again. 

Fraud analytics are a powerful solution that helps solve these challenges.

Answering important questions about users and user behavior today requires the use of big data. 

Both fraudsters and customers leave behind a digital footprint with each transaction. Banks have access to data about the device someone is using, their IP address, historic usage, transaction patterns, and more. 

The volume of this data makes manual fraud detection processes inefficient. Traditionally, financial institutions and other organizations fighting fraud have used systems based around simple rules. Rules-based systems are time savers that have eliminated a lot of manual work, but they also have some major drawbacks. They generate a high number of false positives, and they’re not adept at uncovering more complex fraud patterns. 

Fraud analytics uses a combination of data mining, machine learning and artificial intelligence, and algorithms to more quickly draw information out of your centralized data. It can identify patterns and anomalies that translate into insights businesses can use to detect suspicious activity, better understand fraud threats, and shore up anti-fraud defenses.

Fraud analytics has the potential to deliver a lot of value to financial institutions.  

Analytics tools can enhance rules-based systems and other tools, constantly improving detection and controls. In other words, analytics help fraud leaders get more value from both their data and their existing technology. 

It’s easier and faster to find hidden patterns using analytics. By analyzing even the smallest bit of data, this method can identify patterns that would have otherwise gone undetected.

And, because fraud analytics centralizes your data, it can break down data silos that keep you from seeing the full picture.

Graph technology can be a powerful tool when applied to fraud analytics. It enables analysts to identify relationships and patterns within large and complex datasets. Graph data structure lends itself particularly well to this. A graph data model structures data as nodes - individual data points like a name or an account - and relationships, or edges - like a person having an account. The graph model is flexible, so it’s easy to add new data as needed.

In the context of fraud analytics, graphs can be used to represent the connections between entities such as individuals, accounts, and transactions, as well as the attributes of those entities.

Graph algorithms quickly find within your data the answers to many of the questions that arise in a fraud investigation. It uncovers patterns, discrepancies, and anomalies within data that might otherwise go undetected. It’s easy to see if someone is connected to a politically exposed person, who might be the ringleader of a group of fraudsters, or if one fraudster is actually part of a larger network.