Underwriting fraud: the one you never see coming
When insurance people talk about fraud, everyone thinks claims: the inflated invoice, the doctored photo, the fake repair quote. Fair enough, that's where the money goes out.
But fraud often gets in much earlier. Right at underwriting.
The forgotten link in the journey
A fake company registration to insure a fleet. A falsified claims history statement to erase an inconvenient loss record. A doctored certificate to unlock a coverage or a better rate. A borrowed identity to get around a cancellation.
None of these documents raise a flag, because at underwriting, nobody is really looking. Controls are designed for compliance, not for fraud. And commercial pressure works against vigilance: an underwriting journey is supposed to be fast, smooth, frictionless. Every additional check is seen as a drag on conversion.
The result: the fraudster walks into the portfolio through the front door.
A fraudster caught at underwriting is a claim you never pay
That's the whole economics of the issue. A fraud intercepted at claims stage has already cost you: file handling, loss adjusting, review time, sometimes a payout made before detection.
The same fraud caught at underwriting costs the price of an automated document analysis. A few seconds of processing versus thousands of euros in fraudulent claims.
And the intelligence gathered at underwriting doesn't evaporate: a profile flagged as risky upstream informs the analysis of every future claim it generates. Upstream detection doesn't replace claims-stage controls. It makes them smarter.
The document alone isn't enough. Context is everything
A well-generated fake company record is undetectable on its own. What gives it away is cross-checking: an incorporation date inconsistent with the declared activity, an address that doesn't match, a director who doesn't exist, a revenue figure out of line with the risk being written.
That's why detection can't be a tool sitting next to the journey. It has to live inside it, with access to every piece of data captured along the way, so each supporting document can be checked against what the applicant actually declared.
At Korint, every document uploaded during the underwriting journey is automatically classified, analyzed and cross-checked against the file's data. Signals are aggregated into a risk score that can trigger a verification task, require an override, or feed directly into pricing. The underwriter keeps the decision, but decides with full visibility.
Writing fast and controlling well are no longer at odds
Maybe that's the real takeaway. As long as controls were manual, you had to choose between commercial fluidity and fraud vigilance. With real-time detection embedded in the journey, the 95%+ of legitimate applications sail through without friction, and only suspicious cases get escalated.
The fight against fraud doesn't start at claims. It starts with the very first attachment.
How can insurers detect document fraud in real time?
By embedding detection directly into business workflows rather than checking after the fact. Every submitted document is automatically classified, analysed and cross-checked against the other pieces and the declared data. Signals are aggregated into a fraud score at file level, and only suspicious cases are escalated to the handler, who keeps the final say.
How is artificial intelligence transforming insurance fraud?
Generative AI gives fraudsters unprecedented capabilities, making attacks faster, more convincing and much harder to detect manually. Three major fraud typologies are emerging: automated document fraud (fake medical invoices, AI-generated accident reports with consistent GPS and weather data, synthetic prescriptions, fictitious repair quotes), deepfakes and identity theft (which allow fraudsters to impersonate a policyholder during a video interview or phone call without any particular technical skills), and AI-assisted organised fraud rings that coordinate waves of consistent false claims targeting multiple brokers and insurers simultaneously. In 2024, Alfa recorded 902 million euros of detected insurance fraud, including 656 million in property and casualty, and industry experts estimate that 5 to 10% of claims paid may involve fraudulent cases.
How do next-generation portfolio management tools like Korint use AI to fight fraud?
Next-generation portfolio management platforms like Korint turn artificial intelligence against fraud by operating on three levels. First, surfacing fraud: AI analyses in real time the data from claims, digital journeys and submitted documents, cross-referencing weak signals that human analysis cannot process at this speed and scale. Second, qualifying and framing fraud: some platforms produce a documented risk score together with a clear explanation readable by business teams, which helps prioritise cases for investigation and adjust triggering thresholds by risk segment. Third, eliminating fraud over time: combined with automated rejection actions, these platforms reduce repeat fraud attempts and enable action at the underwriting stage rather than at the point of claims settlement, which is the most effective lever for reducing structural fraud.