5 Steps to Prevent & Detect Fraud Using Data Analytics

The sophistication and complexity of fraud schemes continue to grow and outclass conventional anti-fraud measures. According to the Association of Certified Fraud Examiners (ACFE) 2018 Report to the Nations, organizations that implement preventive data monitoring detect fraudulent activity 58% faster and experience 52% lower losses than organizations that don’t. As these numbers suggest, understanding your data—and analyzing it effectively—can provide significant benefits to your organization’s effort to prevent and detect fraud.

But where do you begin? Global data volumes continue to grow exponentially and this data can be harnessed to quickly identify unusual patterns or red flags. Historically, this was not possible through 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. It’s not.

Here is a five-step plan that will 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.