Fraud remains a huge challenge for governments and inspectors at all levels, as fraudsters today are more successful than ever. Technologies such as analytics and AI have tremendous potential to support investigations.

Why are fraudsters so successful these days? Partly because they seek easy targets, work hard to stay off the radar and ensure that their fraud actions never appear as an outlier on a program dataset with the rule-based analytics typically used by inspectors. That’s why investigators now need next-gen analytic tools that cut across data and program silos, allowing them to fight fraud without disrupting the efficient and timely delivery of benefits and services.

An enterprise approach to fraud detection

Advanced technologies allow inspection services to centralize diverse data into a single dataset, analyze this data holistically to detect anomalies and hidden patterns that may indicate fraud, and calculate fraud propensity at each stage. This enterprise approach to identifying fraud with analytics is something humans cannot do effectively.

How does it work? First, we should note that the data in government programs could be more cohesive and of better quality. This makes it difficult for analysts to pinpoint the cause of fraud. Data management combined with advanced analytics, AI and machine learning can provide high quality and integration across diverse data sources.

In addition, we need automated business rules. Today, fraud investigators use logic based on their experience and best practices. By automating the application of this logic through software, fraud can be detected faster, earlier and more effectively.

Ultimately, predictive modeling based on historical data enables inspectorates to go beyond what has happened and estimate what will happen. It combines multiple analytics methods to improve pattern recognition and detect abnormalities that may indicate current or future fraud.

More efficient audits and investigations

In the end, advanced analytics will enable fraud inspectors to transform their investigative processes, allowing them to:

• Detect fraudulent activities earlier and with greater precision.
• Reduce the costs of detecting and investigating fraud by minimizing false positives.
• Improve the efficiency and productivity of each inspector.
• Gain a consolidated view of fraud risk to improve models as new trends and threats emerge.
• Reduce fraud losses by detecting previously unknown schemes and patterns.

On average, fraud costs are three times the amount detected by investigators, so advanced analytics has great potential to improve audits.

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