5 Steps to Prevent & Detect Fraud Using Data Analytics

In a turbulent economy, businesses that want to stay competitive must move quickly towards digital transformation. But as they embrace new digital processes and systems, the risk for fraud rises–and the sophistication and complexity of fraud schemes continue to grow and outclass conventional anti-fraud measures.

The good news? Harnessing the power of your data improves your organization’s ability to prevent and detect fraud. And here’s a bonus: it can also help you lower costs, increase productivity with automation, and drive efficient growth.

As global data volumes continue to grow exponentially, analytics can help you quickly identify unusual patterns or red flags–capabilities previously not possible with traditional auditing techniques or sampling alone. Management and audit teams need a more powerful analytics platform to identify fraud patterns that may have previously gone undetected. For organizations or groups just starting to use data analytics to prevent or detect fraud, this can seem time-consuming or even impossible–but it’s not. In fact, the right platform can actually help you save time and money and cut complexity with automation, intelligence, and real-time data.

In this whitepaper, we’ll explain how these five steps can help you deploy data analytics as part of your anti-fraud program:

1. Identify fraud risk factors.

2. Identify areas susceptible to fraud schemes.

3. Understand relevant data sources.

4. Mix, match, and analyze the data.

5. Share insights and schedule alerts.