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Fraud analytics

The three minutes guide

Fraud analytics combines analytic technology and techniques with human interaction to help detect potential improper transactions, such as those based on fraud and/or bribery, either before the transactions are completed or after they occur. completed or after they occur.

The process of fraud analytics involves gathering and storing relevant data and mining it for patterns, discrepancies, and anomalies. The findings are then translated into insights that can allow a company to manage potential threats before they occur as well as develop a proactive fraud and bribery detection environment.

These days, nearly everyone engaged in fraud leaves behind a trail of digital fingerprints. This presents a big opportunity for companies to prevent further harm — but it’s often only considered after the damage has been done.

Leaders in fraud prevention are taking advantage of new tools and technologies to harness their data to sniff out instances of fraud, potentially before they fully unfold. This development couldn’t occur at a better time, as events and regulators alike are challenging the controls organizations have used for years. In areas of anti-fraud, anti-bribery, and anti-money laundering, the regulatory environment has tightened. At the same time, fraud, corruption, and abuse are unrelenting — and constantly evolving. It’s a different world out there. And fraud analytics can help make sense of it.

Anomaly detection and rules-based methods have been in widespread use to combat fraud, corruption, and abuse for more than 20 years. They are powerful tools, but they still have their limits. Adding analytics to this mix can significantly expand fraud detection capabilities, enhancing the “white box ” approach of the rules-based method. Not only can analytics tools enhance rules-based testing methods, but they can also help measure performance to standardize and help fine tune controls for constant improvement. That’s a big deal for companies awash in data — data that could be put to better use.

Identify hidden patterns

Unsupervised or non-rules-based analyses driven by analytics technology can uncover new patterns, trends, fraudulent schemes and scenarios that traditional approaches miss.

Enhance and extend existing efforts

Analytics need not replace what you’re already doing — it can be an extra layer to add punch to your existing efforts.

Cross the divide

Fraud analytics can pull data from across your organization into one central platform, helping create a true, enterprise-wide approach.

Measure and improve performance

What’s working? What’s not? With fraud analytics in place, you don’t have to guess. The data tells the story.

Go where the data is

Different parts of your fraud management process generate different types and amounts of data. Start where fraud data is most plentiful and rich.

Examine interdependencies

The most devastating fraudulent activities exploit hidden connections across your organization. By that same token, you need to be able to connect the dots across your data. Analytics can help you look beyond organizational boundaries.

Set off a cultural shift

Fraud management isn’t new — your organization likely has a mature set of processes, methods, and talent to take this on. Analytics will help to change the dynamic. If your team isn’t ready for it, you may not get the value you need from analytics. Make sure your people are prepared.

Time's upIt doesn’t take a massive initiative to get fraud analytics up and running. Many find that it works well to start with a limited project, and then expand from there. It can take as little as a few weeks.

If you’re frustrated that a massive amount of fraud-related information is going unused, it’s worth giving fraud analytics another look.

To learn more about how to get your fraud analytics initiative off to a smart start, please contact Philippe Delcourt or Jordan Brasseur.