10 hours ago
3 Features Every Modern Anti-Fraud System Must Include
Marcin Nowak, board member at Decerto, has 20+ years in insurance, focusing on automation, technology impact and software solutions.
If we compare the insurance industry to the human body, sales would be the heart pumping blood, while claims management would function as the immune system. Without a proper response from the immune system, even the most efficient organs won't prevent the body from collapsing. It's the same in insurance—overreacting to claims can damage customer trust, and trust is the very foundation of this business. Insurance is essentially a promise: when something goes wrong, someone will be there to support you.
Unfortunately, some individuals try to exploit the system. That's why identifying fraudulent actors is crucial—not only to protect the company but also to safeguard honest customers from being affected.
As I've mentioned in previous articles, I'm a strong advocate of iterative improvement rather than turning everything upside down. There's no need to scrap your existing claims management system just because it's weak in fraud detection. You can successfully implement—or integrate—a dedicated solution that feeds fraud probability assessments into your current process for further review by human experts.
This approach aligns with modern IT architecture: leveraging specialized, best-in-class tools and orchestrating them to automate and optimize business processes. A modern, robust anti-fraud system should include AI-driven fraud detection, seamless integration with systems and business-controlled rules and scoring without IT intervention.
AI has matured to the point where it can be used effectively without building models from scratch. While custom models are still valuable, the real breakthrough is AI's ability to 'read' and 'understand' documents in a way that closely resembles human comprehension. It can analyze data in various formats, extract relevant information, recognize context and draw meaningful conclusions.
The first step in fraud detection and claim analysis should be document digitization and data extraction. Once the data is captured, inconsistencies can be flagged—these may indicate potential fraud. The next step is verifying that data against public and internal databases. For instance, if an invoice was issued by a company, the system should check whether that company exists and whether its address and identification numbers match public records.
A truly effective anti-fraud solution should easily integrate with other platforms—whether through out-of-the-box connectors or by enabling the rapid development of new ones. While some developer effort may be needed, the key questions are: How long will it take? How complex is the integration? Is it prone to errors? Having clear answers helps ensure smoother implementation.
Integration with public databases is also essential. In a recent client project, we applied a proprietary algorithm based on advanced matching logic to flag individuals or entities listed on sanction, watchlists or criminal databases. In practice, the system instantly compares customer data against international sources like the Dow Jones Watchlist. It can detect non-obvious similarities, such as variations in name spelling or transliterations—identifying potential matches within milliseconds.
This is perhaps the most critical feature of all. If every change to the fraud detection algorithm has to go through the IT department, you might wait several months before it's implemented. Not because IT is slow—but because they're managing many priorities, and releases are done in scheduled cycles.
Your fraud system should allow business users to create rules, assign weights and react quickly to emerging fraud patterns—without relying on IT for every adjustment.
Fraud detection cannot be a static process. It requires flexibility, speed and autonomy. Only a system designed with these principles can truly protect your organization, reduce losses and maintain trust with your honest customers.
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