The false evidence epidemic is a major setback for claims fraud teams. Driven by easy access to sophisticated digital manipulation tools, the surge in fake documents has left many insurers feeling out of options.
Some are thinking about increasing investigative hours, others are considering adding yet another separate tool to an ever-growing software stack. But there is a third – and far more attractive – choice. One that’s giving early adopters a significant edge in finding and fighting claims fraud. Keep reading to learn more.
- The biggest risk is repeat claims fraud. But why? ⇒
- Why document fraud is a major productivity drain. ⇒
- Can you trust emerging fraudulent document detection tools? ⇒
- Which tools provide the best intelligence? ⇒
The biggest risk is repeat claim fraud. But why?
Today, any financial criminal – organised or opportunist – can spin up a convincing fake document or image to use in a fraudulent claim. But more importantly, they can do so at scale.
The latest AI digital manipulation (and very often, deepfake) tools make it easy for fraudsters to create multiple, high quality false documents which can be submitted in many claims, across many institutions.
As a result, it has never been more convenient to commit repeat claims fraud. After all, why would a financial criminal limit their opportunities to cash in?
Document fraud is seriously slowing fraud teams down.
The prevalence of false evidence is a huge source of frustration for claims fraud teams and insurers at large. Aware that deepfake technologies are now a free-for-all, claims fraud investigators know that false documents are passing through their remit. But they are powerless to stop the problem, or deal with it promptly.
This is because even with fraudulent document detection tools – most of which are AI-led - investigators are missing two critical pieces of information: if the document in question has been seen before, and if it has been used in an organised fraud ring.
So, can you trust fraudulent document detection tools?
Fraudulent document detection solutions are a vital component of a resilient and efficient fraud strategy. However, some solutions offer greater confidence than others, due to how outcomes are generated.
While AI-powered features including metadata extraction, tamper analysis and deepfake detection are critical to uncovering fraudulent documents, wholly AI tools do not use confirmed, syndicated intelligence from real cases. This can result in dangerous blind spots whereby claims fraud investigators cannot answer the two big confirmatory questions:
- Has the document been seen before?
- Has the document been used in a fraud ring?
"Very few claims fraud teams want to progress without confirmatory information. Yet, without syndicated intelligence, investigators are forced to make partially informed fraud decisions".
Which fraudulent document detection tools provide the best intelligence?
The best way to deal with the false evidence surge is to use solutions that combine document syndication with AI. Specifically, machine learning models fed by data consortia and trained on use-case specific data. This approach combines several benefits:
- Advanced AI technologies find manipulations that human eyes simply cannot.
- Referencing enquiries against syndicated document intelligence eliminates manual comparison and research processes.
- When fed with confirmed fraud data, ML models generate trusted risk scoring and probability matching that can be used to automate outcomes – helping claims fraud teams detect fraud faster and efficiently combat repeat offending.
A consortium-AI hybrid solution results in a unique portfolio of features. To ensure your team will get the maximum benefit, compare your shortlisted document verification solutions against the below must-have features checklist:
Feature |
Overview |
Syndicated Matching |
Get critical fraud information that would otherwise be missed by referencing enquiries against confirmed syndicated data. |
Template Repository |
Quickly add or compare known authentic or fraudulent templates, without buckling under manual workload. |
ML Similarity Detection and Matching |
Rapidly determine document similarity with asset fingerprints and AI trained on use case-specific data. Should include risk scoring and probability matching. |
ML Tamper Detection |
Work cases faster in appropriate order with pre-trained AI that offers risk scoring and actionable high-level flags. |
Platform Approach |
Increase team efficiency by consolidating all insight and functionality into one platform – reducing system hopping. |
SynDOC from Synectics is the only fraud-focused document verification tool on the market with syndicated intelligence, the ability to share known authentic or fraudulent templates and ML trained on use case-specific data.
For practical help cutting repeat claims fraud with SynDOC, contact a Synectics Fraud Strategy Consultant here.
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