Fraud remains a huge challenge for governments and inspectors at all levels, as fraudsters today are more successful than ever. Because many cases are so difficult to detect, there is tremendous potential for technologies such as analytics and AI to support investigations.
The social inspectorates in Belgium have recently set their priorities for combating social fraud over the next two years. Among the intended actions is an intensified focus on social dumping in response to recent high-profile cases of illegal employment on construction sites. The goal of these actions is to ensure fair competition and the financing of social security. To this end, there is also a plan to hire an additional 140 inspectors by 2023. But one thing is certain: these inspectors will need advanced technology to detect the often-sophisticated fraud schemes that come their way.
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.
